@article{MTMT:34614040,
	title = {A review of research on remote sensing images shadow detection and application to building extraction},
	url = {https://m2.mtmt.hu/api/publication/34614040},
	author = {Dong, Xueyan and Cao, Jiannong and Zhao, Weiheng},
	doi = {10.1080/22797254.2023.2293163},
	journal-iso = {EUR J REMOTE SENS},
	journal = {EUROPEAN JOURNAL OF REMOTE SENSING},
	volume = {57},
	unique-id = {34614040},
	issn = {2279-7254},
	abstract = {Buildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical location, and other factors. However, they all share a common feature - the presence of shadows. Obtaining accurate data from building shadows can provide a wealth of reliable information for building research. Consequently, it is crucial to review various methods for extracting building shadows, especially deep learning-based methods, to illustrate shadow implementation scenarios in building research: 1) building detection in very high resolution remote sensing images (VHRRSI); 2) building detection in SAR; 3) building change detection; 4) building damage assessment; 5) building height estimation; 6) building shadow removal; 7) other methods (such as building shadow data enhancement, detection of building shadows in ghost images, and conservation of historic buildings). This study discusses the advantages and disadvantages of building shadow detection methods and provides an overview of the datasets and evaluation metrics commonly used in studies of building shadow applications. We hope that this study will serve as a valuable reference for researchers in the field of building shadow studies.},
	keywords = {remote sensing; Deep learning; shadow detection; Building research},
	year = {2024},
	eissn = {2279-7254}
}

@article{MTMT:34525796,
	title = {Semantic Segmentation-Based Building Extraction in Urban Area Using Memory-Efficient Residual Dilated Convolutional Network},
	url = {https://m2.mtmt.hu/api/publication/34525796},
	author = {Ramalingam, Avudaiammal and George, Sam Varghese and Srivastava, Vandita and Alagala, Swarnalatha and Manickam, J. Martin Leo},
	doi = {10.1007/s13369-023-08593-z},
	journal-iso = {ARAB J SCI ENG},
	journal = {ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING},
	volume = {&},
	unique-id = {34525796},
	issn = {2193-567X},
	year = {2024},
	eissn = {2191-4281},
	pages = {&},
	orcid-numbers = {Ramalingam, Avudaiammal/0000-0002-2259-5637}
}

@article{MTMT:34525760,
	title = {Investigations on extraction of buildings from RS imagery using deep learning models},
	url = {https://m2.mtmt.hu/api/publication/34525760},
	author = {Srivastava, Vandita and Avudaiammal, R and V George, Sam},
	doi = {10.1080/01431161.2023.2292016},
	journal-iso = {INT J REMOTE SENS},
	journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
	volume = {45},
	unique-id = {34525760},
	issn = {0143-1161},
	year = {2024},
	eissn = {1366-5901},
	pages = {68-100},
	orcid-numbers = {Srivastava, Vandita/0000-0001-6682-6831; Avudaiammal, R/0000-0002-2259-5637; V George, Sam/0000-0003-1803-464X}
}

@article{MTMT:34288863,
	title = {IBCO-Net: Integrity-Boundary-Corner Optimization in a General Multistage Network for Building Fine Segmentation From Remote Sensing Images},
	url = {https://m2.mtmt.hu/api/publication/34288863},
	author = {Cao, Yungang and Zhang, Shuang and Sui, Baikai and Xie, Yakun and Zhu, Jun},
	doi = {10.1109/TGRS.2023.3310534},
	journal-iso = {IEEE T GEOSCI REMOTE},
	journal = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
	volume = {61},
	unique-id = {34288863},
	issn = {0196-2892},
	abstract = {Building extraction is a significant topic in high-resolution remote sensing. Insufficient integrity, irregular boundaries, and inaccurate corners remain a problem for existing methods. However, individually optimizing one of these aspects may leave problems in others. Unfortunately, few methods consider integrity, boundary, and corner simultaneously. In this study, we propose a three-stage network [integrity-boundary-corner optimization in a general multistage network (IBCO-Net)] incorporating integrity-boundary-corner optimization for fine segmentation of buildings. First, long-range dependent and spatial-continuous (LDSC) blocks are plugged into the decoder to enhance building integrity. Second, the direction field correction module (DFCM) controls the overall shape of the building by learning the direction field and executing an iterative correction algorithm. Finally, the multistrategy point refinement module (MSPRM) selects boundary and corner points for reclassification to further refine the boundary and relocate corners, and a hybrid loss function supervises IBCO-Net to optimize each stage. Comparative experiments were conducted on three datasets: the Massachusetts building dataset, the ISPRS Potsdam dataset, and the dataset of building instances of typical cities in China. We evaluated common pixel-level metrics and object-level boundary and corner metrics, with experimental results showing that IBCO-Net outperforms eight state-of-the-art convolution neural network (CNN) and transformer-based methods. In addition, the generality of the proposed method is demonstrated via its performance by applying nine existing backbone networks.},
	keywords = {Semantic segmentation; building extraction; & nbsp;Boundary and corner optimization; high-resolution remote sensing; multistage network},
	year = {2023},
	eissn = {1558-0644}
}

@article{MTMT:34288864,
	title = {A Method for Regularizing Buildings through Combining Skeleton Lines and Minkowski Addition},
	url = {https://m2.mtmt.hu/api/publication/34288864},
	author = {Chen, Guoqing and Qian, Haizhong},
	doi = {10.3390/ijgi12090363},
	journal-iso = {ISPRS INT J GEO-INFORMATION},
	journal = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
	volume = {12},
	unique-id = {34288864},
	abstract = {With the increasing availability of remote sensing images, the regularization of jagged building outlines extracted from high-resolution remote sensing images has become a current research hotspot. Based on an existing method proposed earlier by this author for extracting the skeleton lines of buildings through integrating vector and raster data using jagged building skeleton lines as the input data, a new method is proposed here for regularizing building outlines through combining the skeleton lines with the Minkowski addition algorithm. Since the size and orientation of the structuring elements remain constant in the traditional morphological method, they can easily lead to large changes in the area between the regularized results and area of the original building. In this work, structuring elements are constructed with the adaptive adjustment of size and orientation. The proposed method has an outstanding ability to maintain the area of the original building. The orthogonal characteristics of the building can be better preserved via rotating the structuring elements. Finally, the angular bisector method is used to dissipate conflicts among the redundant vertices in the building outlines. In comparison to the simplification method used in QGIS software, the method proposed in this paper could reduce the variation in the area while maintaining the orthogonal characteristics of the building more significantly.},
	keywords = {skeleton line; regularization of buildings; Minkowski addition algorithm},
	year = {2023},
	eissn = {2220-9964}
}

@article{MTMT:34288865,
	title = {An Efficient Global Constraint Approach for Robust Contour Feature Points Extraction of Point Cloud},
	url = {https://m2.mtmt.hu/api/publication/34288865},
	author = {Chen, Xijiang and Zhao, Bufan},
	doi = {10.1109/TGRS.2023.3308376},
	journal-iso = {IEEE T GEOSCI REMOTE},
	journal = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
	volume = {61},
	unique-id = {34288865},
	issn = {0196-2892},
	abstract = {The contour feature points of object point clouds are the main features of human perception on target and play an important role in many fields, such as indoor model reconstruction, and object detection and location. In this article, we present a new method to extract the contour feature points of point cloud, which mainly includes two main contents: 1) the conspicuous and inconspicuous boundary points are extracted according to the characteristics of distribution of the azimuth between adjacent vectors in 2-D view and 2) according to the direction of main feature vector, a 2-D projection plane of adjacent points in the bounding sphere is constructed, and the crease points are extracted according to the constraint parameters model of distribution mechanism of adjacent points in the 2-D view. We evaluate the performance of the proposed method using objects of different sizes in real-world scenarios. Simultaneously, the extraction effect of contour feature points is compared with other methods, and the results show that the extraction and antinoise performance of the proposed method are superior to the other methods. Simultaneously, it is suitable not only for regular flat-shaped buildings but also for objects with irregular curvilinear architecture. Moreover, the proposed method involves only one parameter that needs to be tuned, and the parameter can be quickly obtained based on the distance resolution.},
	keywords = {point cloud; Principal component analysis (PCA); boundary points; Feature points; crease points},
	year = {2023},
	eissn = {1558-0644}
}

@article{MTMT:33899036,
	title = {Adversarial patch attacks against aerial imagery object detectors},
	url = {https://m2.mtmt.hu/api/publication/33899036},
	author = {Tang, Guijian and Jiang, Tingsong and Zhou, Weien and Li, Chao and Yao, Wen and Zhao, Yong},
	doi = {10.1016/j.neucom.2023.03.050},
	journal-iso = {NEUROCOMPUTING},
	journal = {NEUROCOMPUTING},
	volume = {537},
	unique-id = {33899036},
	issn = {0925-2312},
	abstract = {Although Deep Neural Networks (DNNs)-based object detectors are widely used in various fields, espe-cially on aerial imagery object detections, it has been observed that a small elaborately designed patch attached to the images can mislead the DNNs-based detectors into producing erroneous output. However, the target detectors being attacked are quite simple, and the attack efficiency is relatively low in previous works, making it not practicable in real scenarios. To address these limitations, a new adversarial patch attack algorithm is proposed in this paper. Firstly, we designed a novel loss function using the intermediate outputs of the models rather than the model's final outputs interpreted by the detection head to optimize adversarial patches. The experiments conducted on the DOTA, RSOD, and NWPU VHR-10 datasets demonstrate that our method can significantly degrade the performance of the detectors. Secondly, we conducted intensive experiments to investigate the impact of different out-puts of the detection model on generating adversarial patches, demonstrating the class score is not as effective as the objectness score. Thirdly, we comprehensively analyzed the attack transferability across different aerial imagery datasets, verifying that the patches generated on one dataset are also effective in attacking another. Moreover, we proposed ensemble training to boost the attack's transferability across models. Our work alarms the application of DNNs-based object detectors in aerial imagery.(c) 2023 Elsevier B.V. All rights reserved.},
	keywords = {aerial imagery; Object Detection; Adversarial patch attacks; Black -box},
	year = {2023},
	eissn = {1872-8286},
	pages = {128-140}
}

@article{MTMT:33899038,
	title = {HiSup: Accurate polygonal mapping of buildings in satellite imagery with hierarchical supervision},
	url = {https://m2.mtmt.hu/api/publication/33899038},
	author = {Xu, Bowen and Xu, Jiakun and Xue, Nan and Xia, Gui-Song},
	doi = {10.1016/j.isprsjprs.2023.03.006},
	journal-iso = {ISPRS J PHOTOGRAMM},
	journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
	volume = {198},
	unique-id = {33899038},
	issn = {0924-2716},
	abstract = {This paper studies the problem of the polygonal mapping of buildings by tackling the issue of mask reversibility, which leads to a notable performance gap between the predicted masks and polygons from the learning-based methods. We addressed such an issue by exploiting the hierarchical supervision (of bottom-level vertices, mid-level line segments, and high-level regional masks) and proposed a novel interaction mechanism of feature embedding sourced from different levels of supervision signals to obtain reversible building masks for polygonal mapping of buildings. As a result, we show that the learned reversible building masks take all the merits of the advances of deep convolutional neural networks for high-performing polygonal mapping of buildings. In the experiments, we evaluated our method on four public benchmarks, including the AICrowd, Open Cities, Shanghai, and Inria datasets. On the AICrowd, Open Cities, and Shanghai datasets, our proposed method obtains unanimous improvements on the metrics of AP, APboundary and PoLiS by large margins. For the Inria dataset, our proposed method also obtains very competitive results on the metrics of IoU and Accuracy. The models and source code are available at https://github.com/SarahwXU/HiSup.},
	keywords = {building extraction; high-resolution satellite imagery; Building vectorization},
	year = {2023},
	eissn = {1872-8235},
	pages = {284-296},
	orcid-numbers = {Xia, Gui-Song/0000-0001-7660-6090}
}

@article{MTMT:33899037,
	title = {BCTNet: Bi-Branch Cross-Fusion Transformer for Building Footprint Extraction},
	url = {https://m2.mtmt.hu/api/publication/33899037},
	author = {Xu, Lele and Li, Ye and Xu, Jinzhong and Zhang, Yue and Guo, Lili},
	doi = {10.1109/TGRS.2023.3262967},
	journal-iso = {IEEE T GEOSCI REMOTE},
	journal = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
	volume = {61},
	unique-id = {33899037},
	issn = {0196-2892},
	abstract = {Building footprint extraction in remote sensing remains challenging due to the diverse appearances of buildings and confusing scenarios. Recently, researchers have revealed that both the globality and locality are vitally important in building footprint extraction tasks and proposed to incorporate the local context and global long-range dependency in the segmentation models. However, inadequate integration of the globality and locality still leads to incomplete, fake, or missing extraction results. To alleviate these problems, a novel segmentation method named bi-branch cross-fusion transformer network (BCTNet) is proposed in this study. Two parallel branches of the convolutional encoder branch (CB) and the transformer encoder branch (TB) are designed to extract multiscale feature maps. A concatenation-then-cross-fusion transformer block (CCTB) is put forward to integrate the locality from the CB and globality from the TB in a cross-fusion way at each stage of the encoding process. Then, an adaptive gating module (AGM) is proposed to gate the feature maps from the CCTB to strengthen the important features while suppressing irrelevant interference information. After that, the segmentation results can be obtained through a simple decoding process. Comprehensive experiments on two benchmark datasets demonstrate that the proposed BCTNet can achieve superior performance compared with the current state-of-the-art (SOTA) segmentation methods.},
	keywords = {Feature extraction; data mining; Convolution; decoding; Gating mechanism; Buildings; Transformer; Task analysis; TRANSFORMERS; Building footprint extraction; cross-fusion},
	year = {2023},
	eissn = {1558-0644},
	orcid-numbers = {Xu, Lele/0000-0002-5000-5788; Li, Ye/0000-0003-1303-7219}
}

@article{MTMT:34288866,
	title = {Bidirectionally greedy framework for unsupervised 3D building extraction from airborne-based 3D meshes},
	url = {https://m2.mtmt.hu/api/publication/34288866},
	author = {Yu, Dayu and Yue, Peng and Ye, Fan and Tapete, Deodato and Liang, Zheheng},
	doi = {10.1016/j.autcon.2023.104917},
	journal-iso = {AUTOMAT CONSTR},
	journal = {AUTOMATION IN CONSTRUCTION},
	volume = {152},
	unique-id = {34288866},
	issn = {0926-5805},
	abstract = {Automatic building information extraction is an active research field in photogrammetry and remote sensing. However, most methods are proposed for supervised segmentation of point clouds or images, which can only capture limited building texture or geometric information, resulting in the obtained buildings being often fragmented. Therefore, we propose a bidirectionally greedy framework to extract spatial-continuous, geometry -complete, fine-textured 3D building models from large-scale 3D meshes captured by airborne in an unsupervised manner. The framework consists of two key steps in opposite directions, namely greedy culling and greedy re-covery. Greedy culling will maximize the removal of non-building primitives based on geometric and textural features. Greedy recovery is designed to maximize the detection of building primitives that are mistakenly removed by the greedy culling, by utilizing topological accessibility. The framework is assessed quantitatively and visually on five high-resolution datasets with different scenes. The results indicate the framework's effec-tiveness in accurately extracting fine-grained building models with complete geometry that can be visualized and analyzed for various 3D applications.},
	keywords = {Photogrammetry; Building segmentation; Geomesh; Realistic 3D building model},
	year = {2023},
	eissn = {1872-7891},
	orcid-numbers = {Yu, Dayu/0000-0003-1720-8302}
}

@article{MTMT:33237735,
	title = {Automatic Building Boundary Extraction From Airborne LiDAR Data Robust to Density Variation},
	url = {https://m2.mtmt.hu/api/publication/33237735},
	author = {dos Santos, Renato Cesar and Pessoa, Guilherme Gomes and Carrilho, Andre Caceres and Galo, Mauricio},
	doi = {10.1109/LGRS.2020.3031397},
	journal-iso = {IEEE GEOSCI REMOTE S},
	journal = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
	volume = {19},
	unique-id = {33237735},
	issn = {1545-598X},
	abstract = {The alpha-shape (alpha-shape) concept, which has its origin in computational geometry, is usually applied in building boundary extraction from airborne LiDAR data. However, the results depend on the appropriate choice of the parameter alpha. Despite several studies in the literature, the adaptive choice of the parameter alpha persists a challenge in boundary extraction, especially when abrupt density variations occur. To overcome this limitation, this letter proposes a new approach combining five estimation strategies. In the proposed method, these strategies are tested sequentially, prioritizing the one that provides greater level of details. The experiments were conducted considering buildings with different characteristics, which were selected from two LiDAR data sets with the average point densities of 12 points/m(2) and 4 points/m(2). The obtained results, presenting F-score. and PoLiS around 98% and 032 m, respectively, indicate the robustness of the proposed method even when abrupt density variation occurs.},
	keywords = {Airborne LiDAR data; alpha-shape algorithm; building boundary extraction; point density variation},
	year = {2022},
	eissn = {1558-0571},
	orcid-numbers = {Galo, Mauricio/0000-0002-0104-9960}
}

@article{MTMT:33095907,
	title = {An Encoder–Decoder Deep Learning Framework for Building Footprints Extraction from Aerial Imagery},
	url = {https://m2.mtmt.hu/api/publication/33095907},
	author = {Khan, Sultan Daud and Alarabi, Louai and Basalamah, Saleh},
	doi = {10.1007/s13369-022-06768-8},
	journal-iso = {ARAB J SCI ENG},
	journal = {ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING},
	volume = {&},
	unique-id = {33095907},
	issn = {2193-567X},
	year = {2022},
	eissn = {2191-4281},
	pages = {&},
	orcid-numbers = {Khan, Sultan Daud/0000-0002-7406-8441}
}

@article{MTMT:33088581,
	title = {A review of building detection from very high resolution optical remote sensing images},
	url = {https://m2.mtmt.hu/api/publication/33088581},
	author = {Li, Jiayi and Huang, Xin and Tu, Lilin and Zhang, Tao and Wang, Leiguang},
	doi = {10.1080/15481603.2022.2101727},
	journal-iso = {GISCI REMOTE SENS},
	journal = {GISCIENCE AND REMOTE SENSING},
	volume = {59},
	unique-id = {33088581},
	issn = {1548-1603},
	year = {2022},
	eissn = {1943-7226},
	pages = {1199-1225}
}

@inproceedings{MTMT:33899039,
	title = {Universal adversarial perturbation for remote sensing images},
	url = {https://m2.mtmt.hu/api/publication/33899039},
	author = {Wang, Qingyu and Feng, Guorui and Yin, Zhaoxia and Luo, Bin},
	booktitle = {2022 IEEE 24TH INTERNATIONAL WORKSHOP ON MULTIMEDIA SIGNAL PROCESSING (MMSP)},
	doi = {10.1109/MMSP55362.2022.9948869},
	unique-id = {33899039},
	abstract = {Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%.},
	keywords = {Deep learning; Encoder-decoder; attention mechanism; remote sensing image; universal adversarial perturbation},
	year = {2022},
	orcid-numbers = {Yin, Zhaoxia/0000-0003-0387-4806}
}

@article{MTMT:33125029,
	title = {A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction},
	url = {https://m2.mtmt.hu/api/publication/33125029},
	author = {Xiao, Xiao and Guo, Wenliang and Chen, Rui and Hui, Yilong and Wang, Jianing and Zhao, Hongyu},
	doi = {10.3390/rs14112611},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {14},
	unique-id = {33125029},
	abstract = {Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets.},
	year = {2022},
	eissn = {2072-4292},
	pages = {2611},
	orcid-numbers = {Guo, Wenliang/0000-0002-1418-4117; Wang, Jianing/0000-0001-6704-1198}
}

@article{MTMT:32165073,
	title = {The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space},
	url = {https://m2.mtmt.hu/api/publication/32165073},
	author = {dos Santos, Renato César and Galo, Mauricio and Carrilho, André Caceres and Pessoa, Guilherme Gomes},
	doi = {10.1007/s12518-021-00371-6},
	journal-iso = {APPL GEOMAT},
	journal = {APPLIED GEOMATICS},
	volume = {&},
	unique-id = {32165073},
	issn = {1866-9298},
	year = {2021},
	eissn = {1866-928X},
	pages = {&},
	orcid-numbers = {dos Santos, Renato César/0000-0003-0263-312X; Galo, Mauricio/0000-0002-0104-9960; Carrilho, André Caceres/0000-0002-0489-2312; Pessoa, Guilherme Gomes/0000-0003-3546-8706}
}

@article{MTMT:32164615,
	title = {Detection of anthropogenic objects based on the spatial characteristics of their contour in aerial image},
	url = {https://m2.mtmt.hu/api/publication/32164615},
	author = {Hammed, Hayder Makki and Hilal Almiahi, Osama Majeed and Shauchuk, Oksana},
	doi = {10.11591/ijeecs.v23.i1.pp206-215},
	journal-iso = {IJEECS},
	journal = {INDONESIAN JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE},
	volume = {23},
	unique-id = {32164615},
	issn = {2502-4752},
	year = {2021},
	eissn = {2502-4760},
	pages = {206}
}

@article{MTMT:32165083,
	title = {Building height estimation via satellite metadata and shadow instance detection},
	url = {https://m2.mtmt.hu/api/publication/32165083},
	author = {Hao, Hanxiang and Baireddy, Sriram and Bartusiak, Emily and Gupta, Mridul and LaTourette, Kevin and Konz, Latisha and Chan, Moses and Comer, Mary L. and Delp, Edward J.},
	doi = {10.1117/12.2585012},
	journal-iso = {PROCEEDINGS OF SPIE},
	journal = {PROCEEDINGS OF SPIE - THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING},
	volume = {11729},
	unique-id = {32165083},
	issn = {0277-786X},
	year = {2021},
	eissn = {1996-756X}
}

@mastersthesis{MTMT:33125045,
	title = {Automatic Object Extraction from Airborne Laser Scanning Point Clouds for Digital Base Map Production},
	url = {https://m2.mtmt.hu/api/publication/33125045},
	author = {Widyaningrum, E.},
	unique-id = {33125045},
	year = {2021}
}

@article{MTMT:31689293,
	title = {A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles},
	url = {https://m2.mtmt.hu/api/publication/31689293},
	author = {Cazzato, Dario and Cimarelli, Claudio and Sanchez-Lopez, Jose Luis and Voos, Holger and Leo, Marco},
	doi = {10.3390/jimaging6080078},
	journal-iso = {J IMAGING},
	journal = {JOURNAL OF IMAGING},
	volume = {6},
	unique-id = {31689293},
	issn = {2313-433X},
	abstract = {The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.},
	keywords = {Computer vision; Deep learning; Unmanned Aerial Vehicles; 2d object detection},
	year = {2020},
	orcid-numbers = {Cazzato, Dario/0000-0002-5472-9150; Cimarelli, Claudio/0000-0002-0414-8278; Sanchez-Lopez, Jose Luis/0000-0001-5018-0925; Leo, Marco/0000-0001-5636-6130}
}

@article{MTMT:31485920,
	title = {Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification},
	url = {https://m2.mtmt.hu/api/publication/31485920},
	author = {Chen, Li and Li, Haifeng and Zhu, Guowei and Li, Qi and Zhu, Jiawei and Huang, Haozhe and Peng, Jian and Zhao, Lin},
	doi = {10.1109/ACCESS.2020.3011639},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {8},
	unique-id = {31485920},
	issn = {2169-3536},
	abstract = {Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis. During recent years, convolutional neural networks (CNNs) have achieved significant success and are widely applied in RSI scene classification. However, crafted images that serve as adversarial examples can potentially fool CNNs with high confidence and are hard for human eyes to interpret. For the increasing security and robust requirements of RSI scene classification, the adversarial example problem poses a serious problem for the classification results derived from systems using CNN models, which has not been fully recognized by previous research. In this study, to explore the properties of adversarial examples of RSI scene classification, we create different scenarios by testing two major attack algorithms (i.e., the fast gradient sign method (FGSM) and basic iterative method (BIM)) trained on different RSI benchmark datasets to fool CNNs (i.e., InceptionV1, ResNet and a simple CNN). In the experiment, our results show that CNNs of RSI scene classification are also vulnerable to adversarial examples, and some of them have a fooling rate of over 80%. These adversarial examples are affected by the architecture of CNNs and the type of RSI dataset. InceptionV1 has a fooling rate of less than 5%, which is lower than the others. Adversarial examples generated on the UCM dataset are easier than other datasets. Importantly, we also find that the classes of adversarial examples have an attack selectivity property. Misclassifications of adversarial examples of RSIs are related to the similarity of the original classes in the CNN feature space. Attack selectivity reveals potential classes of adversarial examples and provides insights into the design of defensive algorithms in future research.},
	keywords = {Feature extraction; remote sensing; Security; Robustness; Computational modeling; Data models; Convolutional neural network; Deep learning; Perturbation methods; remote sensing image; adversarial example},
	year = {2020},
	eissn = {2169-3536},
	pages = {137477-137489}
}

@inproceedings{MTMT:31965034,
	title = {Automatic Building Change Detection Using Multi-Temporal Airborne Lidar Data},
	url = {https://m2.mtmt.hu/api/publication/31965034},
	author = {dos Santos, R. C. and Galo, M. and Carrilho, A. C. and Pessoa, G. G. and de Oliveira, R. A. R.},
	booktitle = {2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS)},
	doi = {10.1109/LAGIRS48042.2020.9165628},
	unique-id = {31965034},
	year = {2020},
	pages = {54-59}
}

@article{MTMT:31485923,
	title = {Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline},
	url = {https://m2.mtmt.hu/api/publication/31485923},
	author = {dos Santos, Renato Cesar and Galo, Mauricio and Habib, Ayman F.},
	doi = {10.3390/rs12121904},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {12},
	unique-id = {31485923},
	abstract = {Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presentingF(score)andPoLiSaround 95% and 0.30 m, respectively.},
	keywords = {LIDAR DATA; building roof boundary regularization; CD-spline; contour modeling},
	year = {2020},
	eissn = {2072-4292}
}

@article{MTMT:31485917,
	title = {Superpixel-based imaging for residential area detection of high spatial resolution remote sensing imagery},
	url = {https://m2.mtmt.hu/api/publication/31485917},
	author = {Li, Junjun and Cao, Jiannong and Zhu, Yingying and Feyissa, Muleta Ebissa and Chen, Beibei},
	doi = {10.1117/1.JRS.14.026507},
	journal-iso = {J APPL REMOTE SENS},
	journal = {JOURNAL OF APPLIED REMOTE SENSING},
	volume = {14},
	unique-id = {31485917},
	issn = {1931-3195},
	abstract = {The precise and efficient location of residential areas using high spatial resolution remote sensing imagery is a popular research area in the field of Earth observation. Most of the existing approaches are supervised or semisupervised and use data training. Among the unsupervised approaches, corner density-based mapping using kernel density estimate has been widely employed to predict the presence of built-up areas. However, it is computationally time-consuming and the statistical threshold segmentation makes it difficult to obtain a stable and accurate output. To overcome this deficiency, a new two-stage object-oriented residential area extraction scheme was designed. First, a set of corners was extracted using the Gabor filter bank with structural tensor analysis to indicate candidate buildings. Then, instead of pixel units, our method takes superpixel-based image partitions as the primary calculation elements, and an object-oriented weighted sparse spatial voting technique was proposed to accelerate the generation of a residential area presence index. It was demonstrated that the superpixel-based voting strategy was not only efficient in accelerating the calculation process, but it also reduced the false negative rate in the final detection result. Second, a graph-cut method was employed to address the residential area segmentation by integrating a density map as a prior cue that preserves the boundary accuracy better than traditional statistical threshold methods. The effectiveness of the proposed method was evaluated using a series of experiments on the sets of high-resolution Google Earth, IKONOS, and GaoFen-2 (GF2) satellite imagery. The results showed that the proposed approach outperforms the existing algorithms in terms of computational speed and accuracy. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)},
	keywords = {Graph cut; Superpixel; Gabor transform; high spatial resolution image; residential area extraction; weighted sparse spatial voting},
	year = {2020},
	eissn = {1931-3195}
}

@article{MTMT:31485921,
	title = {Building outline extraction from ALS point clouds using medial axis transform descriptors},
	url = {https://m2.mtmt.hu/api/publication/31485921},
	author = {Widyaningrum, Elyta and Peters, Ravi Y. and Lindenbergh, Roderik C.},
	doi = {10.1016/j.patcog.2020.107447},
	journal-iso = {PATTERN RECOGN},
	journal = {PATTERN RECOGNITION},
	volume = {106},
	unique-id = {31485921},
	issn = {0031-3203},
	abstract = {Automatic building extraction and delineation from airborne LiDAR point cloud data of urban environments is still a challenging task due to the variety and complexity at which buildings appear. The Medial Axis Transform (MAT) is able to describe the geometric shape and topology of an object, but has never been applied for building roof outline extraction. It represents the shape of an object by its centerline, or skeleton structure instead of its boundary. Notably, end points of the MAT in principle coincide with corner points of building outlines. However, the MAT is sensitive to small boundary irregularities, which makes shape detection in airborne point clouds challenging. We propose a robust MAT-based method for detecting building corner points, which are then connected to form a building boundary polygon. First, we approximate the 2D MAT of a set of building edge points acquired by the alpha-shape algorithm to derive a so-called building roof skeleton. We then propose a hierarchical corner-aware segmentation to cluster skeleton points based on their properties which are the so-called separation angle, radius of the maximally inscribe circle, and defining edge point indices. From each segment, a corner point is then estimated by extrapolating the position of the zero radius inscribed circle based on the skeleton point positions within the segment. Our experiment uses point cloud datasets of Makassar, Indonesia and EYE-Amsterdam, The Netherlands. The average positional accuracy of the building outline results for Makassar and EYE-Amsterdam is 65 cm and 70 cm, respectively, which meet one-meter base map accuracy criteria. The results imply that skeletonization is a promising tool to extract relevant geometric information on e.g. building outlines even from far from perfect geographical point cloud data. (C) 2020 The Author(s). Published by Elsevier Ltd.},
	keywords = {SKELETON; Segmentation; point cloud; Medial axis transform; Building outline},
	year = {2020},
	eissn = {1873-5142},
	orcid-numbers = {Widyaningrum, Elyta/0000-0002-1563-9996}
}

@article{MTMT:32239310,
	title = {Building extraction from high-resolution remote sensing images based on Grabcut with automatic selection of foreground and background samples},
	url = {https://m2.mtmt.hu/api/publication/32239310},
	author = {Zhang, K. and Chen, H. and Xiao, W. and Sheng, Y. and Su, D. and Wan, P.},
	doi = {10.14358/PERS.86.4.235},
	journal-iso = {PHOTOGRAMM ENG REM S},
	journal = {PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING},
	volume = {86},
	unique-id = {32239310},
	issn = {0099-1112},
	year = {2020},
	eissn = {2374-8079},
	pages = {235-245}
}

@article{MTMT:31208256,
	title = {Extração Automática de Contornos de Edificações a partir de Dados LiDAR Aerotransportado},
	url = {https://m2.mtmt.hu/api/publication/31208256},
	author = {Carrilho, André Caceres and Dos Santos, Renato César and Pessoa, Guilherme Gomes and Galo, Mauricio},
	doi = {10.14393/rbcv71n3-46515},
	journal-iso = {REVISTA BRASILEIRA DE CARTOGRAFIA / BRAZIL J CARTOGRAPHY},
	journal = {REVISTA BRASILEIRA DE CARTOGRAFIA / BRAZILIAN JOURNAL OF CARTOGRAPHY},
	volume = {71},
	unique-id = {31208256},
	issn = {0560-4613},
	year = {2019},
	pages = {832-855}
}

@article{MTMT:30905896,
	title = {Active Cues Collection and Integration for Building Extraction With High-Resolution Color Remote Sensing Imagery},
	url = {https://m2.mtmt.hu/api/publication/30905896},
	author = {Hao, Lechuan and Zhang, Ye and Cao, Zhimin},
	doi = {10.1109/JSTARS.2019.2926738},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {12},
	unique-id = {30905896},
	issn = {1939-1404},
	abstract = {Building extraction from high-resolution color remote sensing imagery (HRCRSI) is important in city planning, building reconstruction, and other applications. However, the performance of the state-of-the-art methods is often passively dependent on the accuracy and reliability of the initial edges/regions acquired by general edge/region extraction methods. But the performance of these general methods is always sensitive to unavoidable noise and interferences, especially for the HRCRSI imagery. Furthermore, structural information of the target (e.g., buildings herein) is not fully utilized in these general methods, which is undoubtedly a useful clue to reducing the effects of noise and interference. Therefore, undesired results are inevitable for building extraction methods conducted with a passive or semiactive manner. In this paper, in order to alleviate this problem to a certain extent, we carried out the building extraction task in a completely active manner: 1) under the guidance of the visual perception theory, cues of building edges and regions are actively collected by considering building priors related to main direction and color; and 2) based on knowledge about building shape widely accepted in the literature, cues of the obtained building edges and regions are actively integrated for final building extraction. Experimental results on three benchmark datasets, including aerial and high-resolution optical satellite images, illustrate that the proposed active method can achieve the expected building extraction results.},
	keywords = {Edge extraction; building extraction; main direction; region extraction},
	year = {2019},
	eissn = {2151-1535},
	pages = {2675-2694},
	orcid-numbers = {Cao, Zhimin/0000-0002-3679-6288}
}

@article{MTMT:3419087,
	title = {Direction Selective Contour Detection for Salient Objects},
	url = {https://m2.mtmt.hu/api/publication/3419087},
	author = {Manno-Kovács, Andrea},
	doi = {10.1109/TCSVT.2018.2804438},
	journal-iso = {IEEE T CIRC SYST VID},
	journal = {IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY},
	volume = {29},
	unique-id = {3419087},
	issn = {1051-8215},
	year = {2019},
	eissn = {1558-2205},
	pages = {375-389},
	orcid-numbers = {Manno-Kovács, Andrea/0000-0002-9392-379X}
}

@article{MTMT:30343501,
	title = {Image Based Robust Target Classification for Passive ISAR},
	url = {https://m2.mtmt.hu/api/publication/30343501},
	author = {Manno-Kovács, Andrea and Giusti, E and Berizzi, F and Kovács, Levente Attila},
	doi = {10.1109/JSEN.2018.2876911},
	journal-iso = {IEEE SENS J},
	journal = {IEEE SENSORS JOURNAL},
	volume = {19},
	unique-id = {30343501},
	issn = {1530-437X},
	year = {2019},
	eissn = {1558-1748},
	pages = {268-276},
	orcid-numbers = {Manno-Kovács, Andrea/0000-0002-9392-379X; Kovács, Levente Attila/0000-0001-7792-4947}
}

@article{MTMT:32009443,
	title = {Urban human settlements monitoring model and its application based on multi- source spatial data fusion},
	url = {https://m2.mtmt.hu/api/publication/32009443},
	author = {Ting, C. and Wenbin, W. and Jianjun, H. and Yuexia, Q. and Feng, U. and Qiang, W.},
	doi = {10.5846/stxb201809111948},
	journal-iso = {ACTA ECOL SINICA},
	journal = {SHENGTAI XUEBAO / ACTA ECOLOGICA SINICA},
	volume = {39},
	unique-id = {32009443},
	issn = {1000-0933},
	year = {2019},
	eissn = {1872-2032},
	pages = {1300-1308}
}

@inproceedings{MTMT:31576979,
	title = {An Improved Fully Convolutional Network for Learning Rich Building Features},
	url = {https://m2.mtmt.hu/api/publication/31576979},
	author = {Wang, Shuang and Zhou, Ligang and He, Pei and Quan, Dou and Zhao, Qing and Liang, Xuefeng and Hou, Biao},
	booktitle = {2019 IEEE International Geoscience and Remote Sensing Symposium IGARSS 2019},
	doi = {10.1109/IGARSS.2019.8898460},
	unique-id = {31576979},
	abstract = {Many efficient approaches are proposed to detect building in remote sensing images. In this paper, in order to learning rich building features better, we propose a full convolutional network with dense connection. There contributions are made: 1) To strengthen feature propagation, an improved dense network is introduced to the full convolution network. 2) We have designed top-down short connections to facilitate the fusion of high and low feature information. 3) In addition, we add the weighted cross entropy edge loss function to make the network pay more attention to building edge in detail. Experiments show that the proposed method achieves excellent performance on the remote sensing image data taken by the QuickBird satellite.},
	keywords = {Building detection; FCN; DenseNet; feature fusion; Edge Loss},
	year = {2019},
	pages = {6444-6447}
}

@article{MTMT:30799345,
	title = {Integrated Local Features to Detect Building Locations in High-Resolution Satellite Imagery},
	url = {https://m2.mtmt.hu/api/publication/30799345},
	author = {Yousefiyan, Farzaneh and Ebadi, Hamid and Sedaghat, Amin},
	doi = {10.1007/s12524-019-01001-w},
	journal-iso = {PHOTONIRVACHAK-J IND},
	journal = {PHOTONIRVACHAK / JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING},
	volume = {47},
	unique-id = {30799345},
	issn = {0255-660X},
	year = {2019},
	eissn = {0974-3006},
	pages = {1375-1389}
}

@article{MTMT:27480805,
	title = {A Building Extraction Method via Graph Cuts Algorithm by Fusion of LiDAR Point Cloud and Orthoimage},
	url = {https://m2.mtmt.hu/api/publication/27480805},
	author = {DU, Shouji and ZOU, Zhengrong and ZHANG, Yunsheng and HE, Xue and WANG, Jingxue},
	doi = {10.11947/j.AGCS.2018.20160534},
	journal-iso = {ACTA GEODAET CARTOGR SINICA},
	journal = {ACTA GEODAETICA ET CARTOGRAPHICA SINICA},
	volume = {47},
	unique-id = {27480805},
	issn = {1001-1595},
	year = {2018},
	pages = {519-527}
}

@article{MTMT:27270945,
	title = {Real-time video stabilization for fast-moving vehicle cameras},
	url = {https://m2.mtmt.hu/api/publication/27270945},
	author = {Hu, Wu-Chih and Chen, Chao-Ho and Chen, Tsong-Yi and Peng, Min-Yang and Su, Yi-Jen},
	doi = {10.1007/s11042-016-4291-4},
	journal-iso = {MULTIMED TOOLS APPL},
	journal = {MULTIMEDIA TOOLS AND APPLICATIONS: AN INTERNATIONAL JOURNAL},
	volume = {77},
	unique-id = {27270945},
	issn = {1380-7501},
	year = {2018},
	eissn = {1573-7721},
	pages = {1237-1260}
}

@inproceedings{MTMT:3368420,
	title = {Automatic Target Classification in Passive ISAR Range-Crossrange Images},
	url = {https://m2.mtmt.hu/api/publication/3368420},
	author = {Manno-Kovács, Andrea and Giusti, E and Berizzi, F and Kovács, Levente Attila},
	booktitle = {2018 IEEE Radar Conference (Radarconf’18)},
	doi = {10.1109/RADAR.2018.8378558},
	unique-id = {3368420},
	abstract = {This paper presents a method for automatic analysis of passive radar 2D ISAR images to evaluate the possibilities and capabilities of image feature based target extraction and classification. The goal is to extend signal processing based detection and recognition methods with image information. The presented method is fast, easily embeddable and extendable, works near real-time, and we show its viability for classification using real passive 2D ISAR images.},
	year = {2018},
	pages = {206-211},
	orcid-numbers = {Manno-Kovács, Andrea/0000-0002-9392-379X; Kovács, Levente Attila/0000-0001-7792-4947}
}

@article{MTMT:32009447,
	title = {Center-Point-Guided Proposal Generation for Detection of Small and Dense Buildings in Aerial Imagery},
	url = {https://m2.mtmt.hu/api/publication/32009447},
	author = {Shu, Z. and Hu, X. and Sun, J.},
	doi = {10.1109/LGRS.2018.2822760},
	journal-iso = {IEEE GEOSCI REMOTE S},
	journal = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
	volume = {15},
	unique-id = {32009447},
	issn = {1545-598X},
	year = {2018},
	eissn = {1558-0571},
	pages = {1100-1104}
}

@article{MTMT:27686231,
	title = {Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors.},
	url = {https://m2.mtmt.hu/api/publication/27686231},
	author = {Xu, S and Pan, X and Li, E and Wu, B and Bu, S and Dong, W and Xiang, S and Zhang, X},
	doi = {10.1109/TGRS.2018.2850972},
	journal-iso = {IEEE T GEOSCI REMOTE},
	journal = {IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING},
	volume = {56},
	unique-id = {27686231},
	issn = {0196-2892},
	year = {2018},
	eissn = {1558-0644},
	pages = {7369-7387}
}

@article{MTMT:31965052,
	title = {Shadow extraction of building using fusion of edge and point feature orientation from high resolution satellite imagery},
	url = {https://m2.mtmt.hu/api/publication/31965052},
	author = {Yousefiyan, Farzane and Ebadi, Hamin and Sedaghat, Amin},
	doi = {10.29252/jgit.6.3.123},
	journal-iso = {jgit},
	journal = {Journal of Geospatial Information Technology},
	volume = {6},
	unique-id = {31965052},
	issn = {2008-9635},
	year = {2018},
	pages = {123-145}
}

@article{MTMT:3239014,
	title = {An Embedded Marked Point Process Framework for Three-Level Object Population Analysis},
	url = {https://m2.mtmt.hu/api/publication/3239014},
	author = {Benedek, Csaba},
	doi = {10.1109/TIP.2017.2716181},
	journal-iso = {IEEE T IMAGE PROCESS},
	journal = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
	volume = {26},
	unique-id = {3239014},
	issn = {1057-7149},
	year = {2017},
	eissn = {1941-0042},
	pages = {4430-4445},
	orcid-numbers = {Benedek, Csaba/0000-0003-3203-0741}
}

@article{MTMT:26671635,
	title = {Automatic building extraction from LiDAR data fusion of point and grid-based features},
	url = {https://m2.mtmt.hu/api/publication/26671635},
	author = {Du, S and Zhang, Y and Zou, Z and Xu, S and He, X and Chen, S},
	doi = {10.1016/j.isprsjprs.2017.06.005},
	journal-iso = {ISPRS J PHOTOGRAMM},
	journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
	volume = {130},
	unique-id = {26671635},
	issn = {0924-2716},
	year = {2017},
	eissn = {1872-8235},
	pages = {294-307}
}

@article{MTMT:32009454,
	title = {Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment},
	url = {https://m2.mtmt.hu/api/publication/32009454},
	author = {Kakooei, M. and Baleghi, Y.},
	doi = {10.1080/01431161.2017.1294780},
	journal-iso = {INT J REMOTE SENS},
	journal = {INTERNATIONAL JOURNAL OF REMOTE SENSING},
	volume = {38},
	unique-id = {32009454},
	issn = {0143-1161},
	year = {2017},
	eissn = {1366-5901},
	pages = {2511-2534}
}

@article{MTMT:32009459,
	title = {Determination of building age for Istanbul buildings to be used for the earthquake damage analysis according to structural codes by using aerial and satellite images in GIS},
	url = {https://m2.mtmt.hu/api/publication/32009459},
	author = {Konukcu, B.E. and Karaman, H. and Şahin, M.},
	doi = {10.1007/s11069-016-2666-5},
	journal-iso = {NAT HAZARDS},
	journal = {NATURAL HAZARDS},
	volume = {85},
	unique-id = {32009459},
	issn = {0921-030X},
	year = {2017},
	eissn = {1573-0840},
	pages = {1811-1834}
}

@article{MTMT:26260071,
	title = {Building Extraction from Remotely Sensed Images by Integrating Saliency Cue},
	url = {https://m2.mtmt.hu/api/publication/26260071},
	author = {Li, E and Xu, S and Meng, W and Zhang, X},
	doi = {10.1109/JSTARS.2016.2603184},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {10},
	unique-id = {26260071},
	issn = {1939-1404},
	year = {2017},
	eissn = {2151-1535},
	pages = {906-919}
}

@CONFERENCE{MTMT:3287277,
	title = {Lightweight Monocular Obstacle Avoidance by Salient Feature Fusion},
	url = {https://m2.mtmt.hu/api/publication/3287277},
	author = {Manno-Kovács, Andrea and Kovács, Levente Attila},
	booktitle = {ICCV 2017: International Conference on Computer Vision},
	doi = {10.1109/ICCVW.2017.92},
	unique-id = {3287277},
	year = {2017},
	pages = {734-741},
	orcid-numbers = {Manno-Kovács, Andrea/0000-0002-9392-379X; Kovács, Levente Attila/0000-0001-7792-4947}
}

@article{MTMT:26260070,
	title = {Shape-Based Building Detection in Visible Band Images Using Shadow Information},
	url = {https://m2.mtmt.hu/api/publication/26260070},
	author = {Ngo, T-T and Mazet, V and Collet, C and De Fraipont, P},
	doi = {10.1109/JSTARS.2016.2598856},
	journal-iso = {IEEE J-STARS},
	journal = {IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING},
	volume = {10},
	unique-id = {26260070},
	issn = {1939-1404},
	year = {2017},
	eissn = {2151-1535},
	pages = {920-932}
}

@article{MTMT:27134908,
	title = {Multifeature fusion for automatic building change detection in wide-area imagery},
	url = {https://m2.mtmt.hu/api/publication/27134908},
	author = {Prince, D and Sidike, P and Essa, A and Asari, V},
	doi = {10.1117/1.JRS.11.026040},
	journal-iso = {J APPL REMOTE SENS},
	journal = {JOURNAL OF APPLIED REMOTE SENSING},
	volume = {11},
	unique-id = {27134908},
	issn = {1931-3195},
	year = {2017},
	eissn = {1931-3195}
}

@article{MTMT:32009453,
	title = {Satellite image-based ancient dwelling fingerprint detection algorithm},
	url = {https://m2.mtmt.hu/api/publication/32009453},
	author = {Shen, L. and Yang, F.},
	doi = {10.1134/S1054661817030282},
	journal-iso = {PATT RECOG IMAGE ANAL},
	journal = {PATTERN RECOGNITION AND IMAGE ANALYSIS},
	volume = {27},
	unique-id = {32009453},
	issn = {1054-6618},
	year = {2017},
	eissn = {1555-6212},
	pages = {610-617}
}

@article{MTMT:27271308,
	title = {Real-time Scene Understanding for UAV Imagery based on Deep Convolutional Neural Networks},
	url = {https://m2.mtmt.hu/api/publication/27271308},
	author = {Sheppard, Clay and Rahnemoonfar, Maryam},
	doi = {10.1109/IGARSS.2017.8127435},
	journal-iso = {IEEE INT SYMP GEOSCI REMOTE SENS IGARSS},
	journal = {IEEE INTERNATIONAL SYMPOSIUM ON GEOSCIENCE AND REMOTE SENSING IGARSS},
	volume = {2017},
	unique-id = {27271308},
	issn = {2153-6996},
	year = {2017},
	eissn = {2153-7003},
	pages = {2243-2246}
}

@article{MTMT:26500623,
	title = {A Hybrid Approach for Three-Dimensional Building Reconstruction in Indianapolis from LiDAR Data},
	url = {https://m2.mtmt.hu/api/publication/26500623},
	author = {Zheng, Yuanfan and Weng, Qihao and Zheng, Yaoxing},
	doi = {10.3390/rs9040310},
	journal-iso = {REMOTE SENS-BASEL},
	journal = {REMOTE SENSING},
	volume = {9},
	unique-id = {26500623},
	year = {2017},
	eissn = {2072-4292},
	pages = {310}
}

@article{MTMT:26231637,
	title = {Satellite images analysis for shadow detection and building height estimation},
	url = {https://m2.mtmt.hu/api/publication/26231637},
	author = {Liasis, Gregoris and Stavrou, Stavros},
	doi = {10.1016/j.isprsjprs.2016.07.006},
	journal-iso = {ISPRS J PHOTOGRAMM},
	journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
	volume = {119},
	unique-id = {26231637},
	issn = {0924-2716},
	year = {2016},
	eissn = {1872-8235},
	pages = {437-450}
}

@article{MTMT:25962911,
	title = {A GIS-based method for modeling urban-climate parameters using automated recognition of shadows cast by buildings},
	url = {https://m2.mtmt.hu/api/publication/25962911},
	author = {Peeters, Aviva},
	doi = {10.1016/j.compenvurbsys.2016.05.006},
	journal-iso = {COMPUT ENVIRON URBAN},
	journal = {COMPUTERS ENVIRONMENT AND URBAN SYSTEMS},
	volume = {59},
	unique-id = {25962911},
	issn = {0198-9715},
	year = {2016},
	eissn = {1873-7587},
	pages = {107-115}
}