{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:05:06Z","timestamp":1780322706538,"version":"3.54.1"},"reference-count":68,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms.<\/jats:p>","DOI":"10.3390\/rs13245094","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"5094","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":193,"title":["S2Looking: A Satellite Side-Looking Dataset for Building Change Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7957-4442","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"first","affiliation":[{"name":"Beijing Institute of Remote Sensing, Beijing 100011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Lu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing, Beijing 100011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6418-3761","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghai","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Resource and Environment, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiabao","family":"Yue","sequence":"additional","affiliation":[{"name":"Institute of Resource and Environment, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouye","family":"Lv","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing, Beijing 100011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bitao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing, Beijing 100011, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","first-page":"187","article-title":"Evaluation of change detection techniques for monitoring land-cover changes: A case study in new Burg El-Arab area","volume":"50","author":"Afify","year":"2011","journal-title":"World Pumps"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TGRS.2012.2195727","article-title":"Updating Land-Cover Maps by Classification of Image Time Series: A Novel Change-Detection-Driven Transfer Learning Approach","volume":"51","author":"Demir","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"464","article-title":"Land cover change detection at coarse spatial scales based on iterative estimation and previous state information","volume":"95","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1109\/JSTARS.2016.2541678","article-title":"Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral\u2014Spatial Features","volume":"9","author":"Tan","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s11119-012-9270-9","article-title":"Understanding the errors in input prescription maps based on high spatial resolution remote sensing images","volume":"13","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LGRS.2017.2763182","article-title":"Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis","volume":"15","author":"Zhang","year":"2017","journal-title":"IEEE Geoence Remote Sens. Lett."},{"key":"ref_10","first-page":"15","article-title":"Unsupervised change detection in VHR remote sensing imagery\u2014An object-based clustering approach in a dynamic urban environment","volume":"54","author":"Leichtle","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model","volume":"18","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7232","DOI":"10.1109\/TGRS.2020.2981051","article-title":"A Feature Difference Convolutional Neural Network-Based Change Detection Method","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3416","DOI":"10.1109\/TGRS.2009.2022633","article-title":"Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model","volume":"47","author":"Benedek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Benedek, C., and Szir\u00e1Nyi, T. (2008, January 8\u201311). A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761658"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bourdis, N., Marraud, B., and Sahbi, H. (2011, January 24\u201329). Constrained optical flow for aerial image change detection. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6050150"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Alkhelaiwi, M., Boulila, W., Ahmad, J., Koubaa, A., and Driss, M. (2021). An Efficient Approach Based on Privacy-Preserving Deep Learning for Satellite Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13112221"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/10095020.2020.1838957","article-title":"China\u2019s high-resolution optical remote sensing satellites and their mapping applications","volume":"24","author":"Li","year":"2021","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.14358\/PERS.73.1.25","article-title":"New Methodologies for True Orthophoto Generation","volume":"73","author":"Habib","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","unstructured":"Weir, N., Lindenbaum, D., Bastidas, A., Etten, A.V., and Tang, H. (November, January 27). SpaceNet MVOI: A Multi-View Overhead Imagery Dataset. Proceedings of the IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/TGRS.2018.2886643","article-title":"Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images","volume":"57","author":"Saha","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, M., Tan, K., Jia, X., Wang, X., and Chen, Y. (2020). A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12020205"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/LGRS.2018.2869608","article-title":"Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection","volume":"16","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Caye Daudt, R., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully Convolutional Siamese Networks for Change Detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451652"},{"key":"ref_33","first-page":"5603216","article-title":"Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, H., Qi, Z., and Shi, Z. (2021). Remote Sensing Image Change Detection With Transformers. IEEE Trans. Geosci. Remote Sens., 1\u201314.","DOI":"10.1109\/TGRS.2021.3095166"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kaya, M., and Bilge, H.\u015e. (2019). Deep Metric Learning: A Survey. Symmetry, 11.","DOI":"10.3390\/sym11091066"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Walsh, J., Mahony, N.O., Campbell, S., Carvalho, A., and Riordan, D. (2019, January 2\u20133). Deep Learning vs. Traditional Computer Vision. Proceedings of the Computer Vision Conference (CVC) 2019, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jiang, H., Hu, X., Li, K., Zhang, J., Gong, J., and Zhang, M. (2020). PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Computer Vision & Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common Objects in Context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rasouli, A., Kotseruba, I., Kunic, T., and Tsotsos, J. (November, January 27). PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00636"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., and Steger, C. (2019, January 15\u201320). MVTec AD\u2014A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shao, S., Li, Z., Zhang, T., Peng, C., and Sun, J. (November, January 27). Objects365: A Large-Scale, High-Quality Dataset for Object Detection. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00852"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zi, B., Chang, M., Chen, J., Ma, X., and Jiang, Y. (2020, January 12\u201316). WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection. Proceedings of the MM \u201920: The 28th ACM International Conference on Multimedia, New York, NY, USA.","DOI":"10.1145\/3394171.3413769"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shao, D., Zhao, Y., Dai, B., and Lin, D. (2020, January 13\u201319). FineGym: A Hierarchical Video Dataset for Fine-grained Action Understanding. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00269"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Min, W., Liu, L., Wang, Z., Luo, Z., Wei, X., Wei, X., and Jiang, S. (2020, January 12\u201316). ISIA Food-500: A Dataset for Large-Scale Food Recognition via Stacked Global-Local Attention Network. Proceedings of the 28th ACM International Conference on Multimedia, New York, NY, USA.","DOI":"10.1145\/3394171.3414031"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, P., Jiao, B., Yang, L., Yang, Y., Zhang, S., Wei, W., and Zhang, Y. (November, January 27). In Proceedings of the Vehicle Re-identification in Aerial Imagery: Dataset and Approach. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00055"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mandal, M., Kumar, L.K., and Vipparthi, S.K. (2020, January 12\u201316). MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos. Proceedings of the 28th ACM International Conference on Multimedia (MM \u201920), New York, NY, USA.","DOI":"10.1145\/3394171.3413934"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, J., and Ji, S. (2020, January 13\u201319). A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-View Stereo Reconstruction From an Open Aerial Dataset. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00609"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_50","unstructured":"Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., and Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv."},{"key":"ref_51","unstructured":"Chen, S.A., Escay, A., Haberland, C., Schneider, T., Staneva, V., and Choe, Y. (2018). Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Khalid, S., Xiao, W., Trigoni, N., and Markham, A. (2021). Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges. arXiv.","DOI":"10.1109\/CVPR46437.2021.00494"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sun, X., Wang, P., Yan, Z., Xu, F., Wang, R., Diao, W., Chen, J., Li, J., Feng, Y., and Xu, T. (2021). FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery. arXiv.","DOI":"10.1016\/j.isprsjprs.2021.12.004"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Pepe, M., Costantino, D., Alfio, V.S., Vozza, G., and Cartellino, E. (2021). A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100697"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gao, L., Shi, W., Zhu, J., Shao, P., Sun, S., Li, Y., Wang, F., and Gao, F. (2021). Novel Framework for 3D Road Extraction Based on Airborne LiDAR and High-Resolution Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13234766"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-L\u00f3pez, D., Piedelobo, L., Moreno, M.A., Chakhar, A., Ortega-Terol, D., and Gonz\u00e1lez-Aguilera, D. (2021). Design of a Local Nested Grid for the Optimal Combined Use of Landsat 8 and Sentinel 2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13081546"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5078","DOI":"10.1080\/01431161.2017.1420941","article-title":"A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications","volume":"39","author":"Singh","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Miyamoto, T., and Yamamoto, Y. (October, January 26). Using Multimodal Learning Model for Earthquake Damage Detection Based on Optical Satellite Imagery and Structural Attributes. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324464"},{"key":"ref_60","unstructured":"Yamazaki, F., Kouchi, K.I., Kohiyama, M., Muraoka, N., and Matsuoka, M. (2004, January 20\u201324). Earthquake damage detection using high-resolution satellite images. Proceedings of the Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"381","DOI":"10.3390\/rs10030381","article-title":"A Relative Radiometric Calibration Method Based on the Histogram of Side-Slither Data for High-Resolution Optical Satellite Imagery","volume":"10","author":"Mi","year":"2018","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1670","DOI":"10.1109\/JSTARS.2018.2814205","article-title":"A New On-Orbit Geometric Self-Calibration Approach for the High-Resolution Geostationary Optical Satellite GaoFen4","volume":"11","author":"Wang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1111\/j.1477-9730.2011.00665.x","article-title":"Review of developments in geometric modelling for high resolution satellite pushbroom sensors","volume":"27","author":"Poli","year":"2012","journal-title":"Photogramm. Rec."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Barazzetti, L., Brumana, R., Cuca, B., and Previtali, M. (2015, January 16\u201319). Change detection from very high resolution satellite time series with variable off-nadir angle. Proceedings of the SPIE the International Society for Optical Engineering, Paphos, Cyprus.","DOI":"10.1117\/12.2192429"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1111\/phor.12346","article-title":"Automated Registration of SfM-MVS Multitemporal Datasets Using Terrestrial and Oblique Aerial Images","volume":"36","author":"Parente","year":"2021","journal-title":"Photogramm. Rec."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ramu, G., and Babu, S. (2017, January 19\u201320). Image forgery detection for high resolution images using SIFT and RANSAC algorithm. Proceedings of the International Conference on Communication & Electronics Systems, Coimbatore, India.","DOI":"10.1109\/CESYS.2017.8321205"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TIP.2004.838698","article-title":"Image change detection algorithms: A systematic survey","volume":"14","author":"Radke","year":"2005","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5094\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:48:37Z","timestamp":1760168917000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":68,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245094"],"URL":"https:\/\/doi.org\/10.3390\/rs13245094","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}