U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales

Abaidoo, 2019, Monitoring the Extent of Reclamation of Small Scale Mining Areas Using Artificial Neural Networks, Heliyon, 5, e01445, 10.1016/j.heliyon.2019.e01445 Almeida-filho, 2000, Detecting areas disturbed by gold mining activities through JERS-1 SAR images, Roraima State, Brazilian Amazon, Int. J. Remote Sens., 21, 3357, 10.1080/014311600750019967 Asner, 2013, Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring, PNAS, 110, 18454, 10.1073/pnas.1318271110 Benkendorf, 2020, Effects of sample size and network depth on a deep learning approach to species distribution modeling, Ecol. Inf., 60, 101137, 10.1016/j.ecoinf.2020.101137 Breiman, 2001, Random forests, Machine Learning, 5–32 Buda, 2018, A systematic study of the class imbalance problem in convolutional neural networks, Neural Networks, 106, 249, 10.1016/j.neunet.2018.07.011 Buschbacher, K., Ahrens, D., Espeland, M., & Steinhage, V. (2020). Image-based species identification of wild bees using convolutional neural networks. Ecological Informatics, 55(May 2019), 101017. https://doi.org/10.1016/j.ecoinf.2019.101017. Cánovas-García, F., Alonso-Sarría, F., Gomariz-Castillo, F., & Oñate-Valdivieso, F. (2017). Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery. Computers and Geosciences, 103, 1–11. https://doi.org/10.1016/j.cageo.2017.02.012. Chen, 2008, Land degradation monitoring using multi-temporal Landsat TM/ETM data in a transition zone between grassland and cropland of northeast China, Int. J. Remote Sens., 29, 2055, 10.1080/01431160701355280 Chen, 2020, Fine land cover classification in an open pit mining area using optimized support vector machine and world view-3 imagery, Remote Sensing, 12, 12 Conglaton, 1991, A Review of Assessing the Accuarcy of Classifications of Remotely Sensed Data, Remote Sens. Environ., 37, 35, 10.1016/0034-4257(91)90048-B Crum, 2006, Generalized overlap measures for evaluation and validation in medical image analysis, IEEE Trans. Med. Imaging, 25, 1451, 10.1109/TMI.2006.880587 Csurka, G., Larlus, D., & Perronnin, F. (2013). What is a good evaluation measure for semantic segmentation? BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013. https://doi.org/10.5244/C.27.32. Cui, 2020, Semantic Segmentation of Remote Sensing Images Using Transfer Learning and Deep Convolutional Neural Network with Dense Connection, IEEE Access, 8, 116744, 10.1109/ACCESS.2020.3003914 Dlamini, 2019, Monitoring mining disturbance and restoration over RBM site in South Africa using landtrendr algorithm and landsat data, Sustainability (Switzerland), 11, 6916, 10.3390/su11246916 Dwyer, 2018, Analysis ready data: Enabling analysis of the landsat archive, Remote Sensing, 10, 1, 10.3390/rs10091363 Egidarev, 2015, Assessment of the environmental effect of placer gold mining in the Amur river basin, Water Resour., 42, 897, 10.1134/S0097807815070039 Espejo, 2018, Deforestation and forest degradation due to gold mining in the Peruvian Amazon: A 34-year perspective, Remote Sensing, 10, 1 Flood, 2019, Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia, Int. J. Appl. Earth Obs. Geoinf., 82, 101897 Fu, 2017, Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data, Ecol. Ind., 73, 105, 10.1016/j.ecolind.2016.09.029 Gadosey, 2020, SD-UNET: Stripping down U-net for segmentation of biomedical images on platforms with low computational budgets, Diagnostics, 10, 110, 10.3390/diagnostics10020110 Gallwey, 2020, A Sentinel-2 based multispectral convolutional neural network for detecting artisanal small-scale mining in Ghana: Applying deep learning to shallow mining, Remote Sens. Environ., 248, 111970, 10.1016/j.rse.2020.111970 Ghorbanzadeh, 2019, Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection, Remote Sensing, 11, 196, 10.3390/rs11020196 Ghoshal, 2010, Channel and floodplain change analysis over a 100-year period: Lower Yuba river, California. Remote Sensing, 2, 1797, 10.3390/rs2071797 Gilvear, 1995, Image analysis of aerial photography to quantify changes in channel morphology and instream habitat following placer mining in interior Alaska, Freshw. Biol., 34, 389, 10.1111/j.1365-2427.1995.tb00897.x Han, 2020, Comparing fully deep convolutional neural networks for land cover classification with high-spatial-resolution gaofen-2 images, ISPRS Int. J. Geo-Inf., 9, 478, 10.3390/ijgi9080478 He, 2009, Learning from imbalanced data, Studies in Computational Intelligence, 21, 1263 Hurcom, 1998, The NDVI and spectral decomposition for semi-arid vegetation abundance estimation, Int. J. Remote Sens., 19, 3109, 10.1080/014311698214217 Ibrahim, 2020, Small-scale surface mining of gold placers: Detection, mapping, and temporal analysis through the use of free satellite imagery, Int. J. Appl. Earth Obs. Geoinf., 93, 102194 Ioffe, 2015, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 448 Izquierdo-Verdiguier, 2020, An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing, Int. J. Appl. Earth Obs. Geoinf., 88, 102051 Jiang, 2018, Deep neural networks with Elastic Rectified Linear Units for object recognition, Neurocomputing, 275, 1132, 10.1016/j.neucom.2017.09.056 Khan, 2018, Cost-sensitive learning of deep feature representations from imbalanced data, IEEE Trans. Neural Networks Learn. Syst., 29, 3573, 10.1109/TNNLS.2017.2732482 Kingma, 2015, Adam: A method for stochastic optimization, 1 Krizhevsky, 2012, ImageNet Classification with Deep Convolutional Neural Networks, Advances In Neural Information Processing Systems, 1 Liu, 2018, Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data, Remote Sensing, 10, 1, 10.3390/rs10060946 Lobo, 2018, Mapping mining areas in the Brazilian amazon using MSI/Sentinel-2 imagery (2017), Remote Sensing, 10, 1178, 10.3390/rs10081178 Madhuanand, 2021, Deep convolutional neural networks for surface coal mines determination from sentinel-2 images, European Journal of Remote Sensing, 54, 296, 10.1080/22797254.2021.1920341 Marcus, 2002, Mapping of stream microhabitats with high spatial resolution hyperspectral imagery, J. Geogr. Syst., 4, 113, 10.1007/s101090100079 Martins, 2020, Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data, Int. J. Appl. Earth Obs. Geoinf., 93, 102215 Matthews, 2016, People and Fresh Water Ecosystems: Pressures, Responses and Resilience, Aquat. Procedia, 6, 99, 10.1016/j.aqpro.2016.06.012 Maxwell, 2020, Mapping the topographic features of mining-related valley fills using mask R-CNN deep learning and digital elevation data, Remote Sensing, 12, 1, 10.3390/rs12030547 Mhangara, 2020, Monitoring the development of artisanal mines in South Africa, Journal of the Southern African Institute of Mining and Metallurgy, 120, 299, 10.17159/2411-9717/938/2020 Duane Nellis, 1998, Remote sensing of temporal and spatial variations in pool size, suspended sediment, turbidity, and Secchi depth in Tuttle Creek Reservoir, Kansas: 1993, Geomorphology, 21, 281, 10.1016/S0169-555X(97)00067-6 Nelson, 2012, Placer mining along the Fraser River, British Columbia: The geomorphic impact, Bulletin of the Geological Society of America, 124, 1212, 10.1130/B30575.1 Phan, 2020, Land cover classification using google earth engine and random forest classifier-the role of image composition, Remote Sensing, 12, 2411, 10.3390/rs12152411 Nowakowski, 2021, Crop type mapping by using transfer learning, Int. J. Appl. Earth Obs. Geoinf., 98, 102313 Obodai, 2019, Land use/land cover dynamics using landsat data in a gold mining basin-the Ankobra, Ghana, Remote Sens. Appl.: Soc. Environ., 13, 247 Oquab, 2014, Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition, 1717–1724 Rennó, 2008, HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia, Remote Sens. Environ., 112, s3469, 10.1016/j.rse.2008.03.018 Ronneberger, 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation, 9351, 234 Rouse, 1974, Monitoring vegetation systems in the Great Plains with ERTS, NASA special publication, 351, 309 Solana-Gutiérrez, 2017, Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain, Ecol. Model., 360, 260, 10.1016/j.ecolmodel.2017.07.010 Stoian, 2019, Land cover maps production with high resolution satellite image time series and convolutional neural networks: Adaptations and limits for operational systems, Remote Sensing, 11, 1, 10.3390/rs11171986 Sudre, 2017, Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations Sun, 2020, Deep Fusion of Localized Spectral Features and Multi-scale Spatial Features for Effective Classification of Hyperspectral Images, Int. J. Appl. Earth Obs. Geoinf., 91, 102157 Sun, 2017, High-resolution remote sensing data classification over urban areas using random forest ensemble and fully connected conditional random field, ISPRS Int. J. Geo-Inf., 6, 245, 10.3390/ijgi6080245 Syrris, 2019, Evaluation of the Potential of Convolutional Neural Networks and Random Forests for Multi-Class Segmentation of Sentinel-2 Imagery, Remote Sensing, 11, 907, 10.3390/rs11080907 Tharwat, 2018, Classification assessment methods, Applied Computing and Informatics, 17, 168, 10.1016/j.aci.2018.08.003 Tian, 2016, Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China. Remote Sensing, 8, 1 Wang, 2016, Channel bar feature extraction for a mining-contaminated river using high-spatial multispectral remote-sensing imagery, GIScience and Remote Sensing, 53, 283, 10.1080/15481603.2016.1148229 Wang, 2020, Automatic identification and dynamic monitoring of open-pit mines based on improved mask R-CNN and transfer learning, Remote Sensing, 12, 1, 10.3390/rs12213474 Werner, 2019, Assessing impacts of mining: Recent contributions from GIS and remote sensing, Extr. Ind. Soc., 6, 993 Whiteside, 2015, Use of WorldView-2 time series to establish a wetland monitoring program for potential offsite impacts of mine site rehabilitation, Int. J. Appl. Earth Obs. Geoinf., 42, 24 Woldai, 2001, Application of remotely sensed data and GIS in assessing the impact of mining activities on the environment, 75 Wright, 2000, Evaluation of multispectral, fine scale digital imagery as a tool for mapping stream morphology, Geomorphology, 33, 107, 10.1016/S0169-555X(99)00117-8 Xiao, 2018, Real-world plant species identification based on deep convolutional neural networks and visual attention, Ecol. Inf., 48, 117, 10.1016/j.ecoinf.2018.09.001 Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? 1–9. http://arxiv.org/abs/1411.1792. Yu, L., Xu, Y., Xue, Y., Li, X., Cheng, Y., Liu, X., Porwal, A., Holden, E. J., Yang, J., & Gong, P. (2018). Monitoring surface mining belts using multiple remote sensing datasets: A global perspective. Ore Geology Reviews, 101, 675–687. https://doi.org/10.1016/j.oregeorev.2018.08.019. Yu, 2018, Forest type identification by random forest classification combined with SPOT and multitemporal SAR data, J. For. Res., 29, 1407, 10.1007/s11676-017-0530-4 Zhang, 2020, A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images, Int. J. Appl. Earth Obs. Geoinf., 88, 102086