Landslide detection with Mask R-CNN using complex background enhancement based on multi-scale samples

References

• Aksoy B, Ercanoglu M. 2012. Landslide identification and classification by object-based image analysis and fuzzy logic; an example from the Azdavay region (Kastamonu, Turkey). Comput Geosci. 38(1):87–98. doi: 10.1016/j.cageo.2011.05.010.
   Web of Science ®Google Scholar
• Behling R, Roessner S, Kaufmann H, Kleinschmit B. 2014. Automated spatiotemporal landslide mapping over large areas using rapideye time series data. Remote Sens. 6(9):8026–8055. doi: 10.3390/rs6098026.
   Google Scholar
• Bochkovskiy A, Wang C, Liao HM. 2020. YOLOv4: optimal speed and accuracy of object detection. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); Jun 14–19; Virtual. Piscataway, NJ: IEEE.
   Google Scholar
• Brardinoni F, Slaymaker O, Hassan MA. 2003. Landslide inventory in a rugged forested watershed: a comparison between air-photo and field survey data. Geomorphology. 54(3-4):179–196. doi: 10.1016/S0169-555X(02)00355-0.
   Web of Science ®Google Scholar
• Cai H, Chen T, Niu R, Plaza A. 2021. Landslide detection using densely connected convolutional networks and environmental conditions. IEEE J Sel Top Appl Earth Obs Remote Sens. 14:5235–5247. doi: 10.1109/JSTARS.2021.3079196.
   Web of Science ®Google Scholar
• Carvalho OLFD, de Carvalho Júnior OA, Albuquerque AOD, Bem PPD, Silva CR, Ferreira PHG, Moura RDSD, Gomes RAT, Guimarães RF, Borges DL. 2020. Instance segmentation for large, multi-channel remote sensing imagery using Mask-RCNN and a mosaicking approach. Remote Sens. 13(1):39. doi: 10.3390/rs13010039.
   Google Scholar
• Catani F. 2021. Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides. 18(3):1025–1044. doi: 10.1007/s10346-020-01513-4.
   Web of Science ®Google Scholar
• Chen G, Xianju L, Weitao C, Xinwen C, Zhang Y, Shengwei L. 2014. Extraction and application analysis of landslide influential factors based on LiDAR DEM; a case study in the Three Gorges Area, China. Nat Hazards. 74(2):509–526. doi: 10.1007/s11069-014-1192-6.
   Web of Science ®Google Scholar
• Chen W, Li X, Wang Y, Chen G, Liu S. 2014. Forested landslide detection using LiDAR data and the random forest algorithm: a case study of the Three Gorges, China. Remote Sens Environ. 152:291–301. doi: 10.1016/j.rse.2014.07.004.
   Web of Science ®Google Scholar
• Drazba MC, Yan-Richards A, Wilkinson S. 2018. Landslide hazards in Fiji, managing the risk and not the disaster, a literature review. Proc Eng. 212:1334–1338. doi: 10.1016/j.proeng.2018.01.172.
   Google Scholar
• Fu R, He J, Liu G, Li W, Mao J, He M, Lin Y. 2022. Fast seismic landslide detection based on improved Mask R-CNN. Remote Sens. 14(16):3928. doi: 10.3390/rs14163928.
   Google Scholar
• Gao X, Chen T, Niu R, Plaza A. 2021. Recognition and mapping of landslide using a fully convolutional densenet and influencing factors. IEEE J Sel Top Appl Earth Obs Remote Sens. 14:7881–7894. doi: 10.1109/JSTARS.2021.3101203.
   Web of Science ®Google Scholar
• Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 11(2):196. doi: 10.3390/rs11020196.
   Google Scholar
• He KM, Gkioxari G, Dollár P, Girshick R. 2017. Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV); Oct 22–29; Venice, Italy. Piscataway, NJ: IEEE. p. 2980–2988.
   Google Scholar
• Intrieri E, Carlà T, Gigli G. 2019. Forecasting the time of failure of landslides at slope-scale; a literature review. Earth-Sci Rev. 193:333–349. doi: 10.1016/j.earscirev.2019.03.019.
   Web of Science ®Google Scholar
• Ji S. 2019. Dataset collection by Group of Photogrammetry and Computer Vision (GPCV) at Wuhan University-Dataset 4: Bijie Landslide Dataset. Wuhan: Wuhan University.
   Google Scholar
• Ji S, Yu D, Shen C, Li W, Xu Q. 2020. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides. 17(6):1337–1352. doi: 10.1007/s10346-020-01353-2.
   Web of Science ®Google Scholar
• Jiang W, Xi J, Li Z, Ding M, Yang L, Xie D. 2023. Landslide detection and segmentation using Mask R-CNN with simulated hard samples. Geomat Inf Sci WH Uni. 48(12):1931–1942. doi: 10.13203/j.whugis20200692.
   Google Scholar
• Jin KP, Yao LK, Cheng QG, Xing AG. 2019. Seismic landslides hazard zoning based on the modified Newmark model: a case study from the Lushan Earthquake, China. Nat Hazards. 99(1):493–509. doi: 10.1007/s11069-019-03754-6.
   Web of Science ®Google Scholar
• Ju Y, Xu Q, Jin S, Li W, Su Y, Dong X, Guo Q. 2022. Loess landslide detection using object detection algorithms in Northwest China. Remote Sens. 14(5):1182. doi: 10.3390/rs14051182.
   Google Scholar
• Karantanellis E, Marinos V, Vassilakis E, Christaras B. 2020. Object-based analysis using unmanned aerial vehicles (UAVs) for site-specific landslide assessment. Remote Sens. 12(11):1711. doi: 10.3390/rs12111711.
   Google Scholar
• Keyport RN, Oommen T, Martha TR, Sajinkumar KS, Gierke JS. 2018. A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images. Int J Appl Earth Obs Geoinf. 64:1–11. doi: 10.1016/j.jag.2017.08.015.
   Web of Science ®Google Scholar
• Li Y, Cui P, Ye C, Junior JM, Zhang Z, Guo J, Li J. 2021. Accurate prediction of earthquake-induced landslides based on deep learning considering landslide source area. Remote Sens. 13(17):3436. doi: 10.3390/rs13173436.
   Google Scholar
• Li Z, Shi W, Lu P, Yan L, Wang Q, Miao Z. 2016. Landslide mapping from aerial photographs using change detection-based Markov random field. Remote Sens Environ. 187:76–90. doi: 10.1016/j.rse.2016.10.008.
   Web of Science ®Google Scholar
• Liang R, Dai K, Shi X, Guo B, Dong X, Liang F, Tomas R, Wen N, Fan X. 2021. Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) post-disaster landslides based on high-resolution UAV Imagery. Remote Sens. 13(7):1330. doi: 10.3390/rs13071330.
   Google Scholar
• Liu P, Wei Y, Wang Q, Chen Y, Xie J. 2020. Research on post-earthquake landslide extraction algorithm based on improved U-Net model. Remote Sens. 12(5):894. doi: 10.3390/rs12050894.
   Google Scholar
• Liu P, Wei Y, Wang Q, Xie J, Chen Y, Li Z, Zhou H. 2021. A research on landslides automatic extraction model based on the improved Mask R-CNN. ISPRS Int J Geo-Inf. 10(3):168. doi: 10.3390/ijgi10030168.
   PubMed Web of Science ®Google Scholar
• Liu T, Chen T, Niu R, Plaza A. 2021. Landslide detection mapping employing CNN, ResNet, and DenseNet in the three gorges reservoir, China. IEEE J Sel Top Appl Earth Obs Remote Sens. 14:11417–11428. doi: 10.1109/JSTARS.2021.3117975.
   Web of Science ®Google Scholar
• Liu X, Peng Y, Lu Z, Li W, Yu J, Ge D, Xiang W. 2023. Feature-fusion segmentation network for landslide detection using high-resolution remote sensing images and digital elevation model data. IEEE Trans Geosci Remote Sens. 61:1–14. doi: 10.1109/TGRS.2022.3233637.
   Web of Science ®Google Scholar
• Liu Y, Gross L, Li Z, Li X, Fan X, Qi W. 2019. Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling. IEEE Access. 7:128774–128786. doi: 10.1109/ACCESS.2019.2940527.
   Web of Science ®Google Scholar
• Liu Y, Zhou J, Qi W, Li X, Gross L, Shao Q, Zhao Z, Ni L, Fan X, Li Z. 2020. ARC-Net: an efficient network for building extraction from high-resolution aerial images. IEEE Access. 8:154997–155010. doi: 10.1109/ACCESS.2020.3015701.
   Web of Science ®Google Scholar
• Long L, He F, Liu H. 2022. Correction to: the use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River. J Supercomput. 78(9):11974–11974. doi: 10.1007/s11227-022-04353-2.
   Web of Science ®Google Scholar
• Lu H, Ma L, Fu X, Liu C, Wang Z, Tang M, Li N. 2020. Landslides information extraction using object-oriented image analysis paradigm based on deep learning and transfer learning. Remote Sens. 12(5):752. doi: 10.3390/rs12050752.
   Google Scholar
• Lu P, Qin Y, Li Z, Mondini AC, Casagli N. 2019. Landslide mapping from multi-sensor data through improved change detection-based Markov random field. Remote Sens Environ. 231:111235. doi: 10.1016/j.rse.2019.111235.
   Web of Science ®Google Scholar
• Luo S, Tong L, Chen Y, Tan L. 2016. Landslides identification based on polarimetric decomposition techniques using Radarsat-2 polarimetric images. Int J Remote Sens. 37(12):2831–2843. doi: 10.1080/01431161.2015.1041620.
   Web of Science ®Google Scholar
• Lv L, Chen T, Dou J, Plaza A. 2022. A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping. Int J Appl Earth Obs Geoinf. 108:102713. doi: 10.1016/j.jag.2022.102713.
   Web of Science ®Google Scholar
• Manfre LA, de Albuquerque Nobrega RA, Quintanilha JA. 2016. Evaluation of multiple classifier systems for landslide identification in LANDSAT thematic mapper (TM) images. ISPRS Int J Geo-Inf. 5(9):164. doi: 10.3390/ijgi5090164.
   Web of Science ®Google Scholar
• Miao FS, Wu YP, Li LW, Liao K, Zhang LF. 2019. Risk assessment of snowmelt-induced landslides based on GIS and an effective snowmelt model. Nat Hazards. 97(3):1151–1173. doi: 10.1007/s11069-019-03693-2.
   Web of Science ®Google Scholar
• Ullo SL, Mohan A, Sebastianelli A, Ahamed SE, Kumar B, Dwivedi R, Sinha G. 2021. A new Mask R-CNN-based method for improved landslide detection. IEEE J Sel Top Appl Earth Obs Remote Sens. 14:3799–3810. doi: 10.1109/JSTARS.2021.3064981.
   Web of Science ®Google Scholar
• Wang H, Zhang L, Yin K, Luo H, Li J. 2021. Landslide identification using machine learning. Geosci Front. 12(1):351–364. doi: 10.1016/j.gsf.2020.02.012.
   Web of Science ®Google Scholar
• Wang J, Nie G, Gao S, Wu S, Li H, Ren X. 2021. Landslide deformation prediction based on a GNSS time series analysis and recurrent neural network model. Remote Sens. 13(6):1055. doi: 10.3390/rs13061055.
   Google Scholar
• Wang X, Zhang X, Bi J, Zhang X, Deng S, Liu Z, Wang L, Guo H. 2022. Landslide susceptibility evaluation based on potential disaster identification and ensemble learning. Int J Environ Res Public Health. 19(21):14241. doi: 10.3390/ijerph192114241.
   PubMed Web of Science ®Google Scholar
• Xu J, Zhang M, Fan W. 2015. An overview of geological disaster risk assessment. J Catastrophol. 30:130–134.
   Google Scholar
• Yang R, Zhang F, Xia J, Wu C. 2022. Landslide extraction using Mask R-CNN with background-enhancement method. Remote Sens. 14(9):2206. doi: 10.3390/rs14092206.
   Google Scholar
• Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y. 2019. CutMix: regularization strategy to train strong classifiers with localizable features. In IEEE/CVF International Conference on Computer Vision (ICCV); Oct 27–Nov 2; Seoul, South Korea. Piscataway, NJ: IEEE. p. 6022–6031.
   Google Scholar
• Zhang Q. 2019. Convolutional neural network target detection algorithms and application in the field of landslide. Yichang: China Three Gorges University.
   Google Scholar
• Zhang W, Witharana C, Liljedahl AK, Kanevskiy M. 2018. Deep convolutional neural networks for automated characterization of Arctic ice-wedge polygons in very high spatial resolution aerial imagery. Remote Sens. 10(9):1487. doi: 10.3390/rs10091487.
   Google Scholar
• Zhang Y, Guo Z, Wu J, Tian Y, Tang H, Guo X. 2022. Real-time vehicle detection based on improved YOLO v5. Sustainability. 14(19):12274. doi: 10.3390/su141912274.
   Web of Science ®Google Scholar
• Zhou J, Liu Y, Nie G, Cheng H, Yang X, Chen X, Gross L. 2022. Building extraction and floor area estimation at the village level in Rural China via a comprehensive method integrating UAV photogrammetry and the novel EDSANet. Remote Sens. 14(20):5175. doi: 10.3390/rs14205175.
   Google Scholar
• Zhou X, Wen H, Zhang Y, Xu J, Zhang W. 2021. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci Front. 12(5):101211. doi: 10.1016/j.gsf.2021.101211.
   Web of Science ®Google Scholar