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Integrating active learning and crowdsourcing into large-scale supervised landcover mapping algorithms.

, , , and . PeerJ Prepr., (2017)

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Accounting for Training Data Error in Machine Learning Applied to Earth Observations., , , , , , , , , and 10 other author(s). Remote. Sens., 12 (6): 1034 (2020)On the Use of Unmanned Aerial Systems for Environmental Monitoring., , , , , , , , , and 13 other author(s). Remote. Sens., 10 (4): 641 (2018)Integrating active learning and crowdsourcing into large-scale supervised landcover mapping algorithms., , , and . PeerJ Prepr., (2017)High Resolution, Annual Maps of Field Boundaries for Smallholder-Dominated Croplands at National Scales., , , , , , , , , and 17 other author(s). Frontiers Artif. Intell., (2021)Mapping Cropland in Smallholder-Dominated Savannas: Integrating Remote Sensing Techniques and Probabilistic Modeling., , , and . Remote. Sens., 7 (11): 15295-15317 (2015)A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery., , and . CoRR, (2023)Projected climate impacts to South African maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches, , , , , , , and . Glob Change Biol, (Oct 1, 2013)Cropland Mapping in Tropical Smallholder Systems with Seasonally Stratified Sentinel-1 and Sentinel-2 Spectral and Textural Features., , , , , and . Remote. Sens., 15 (12): 3014 (June 2023)Probabilistic Tracking of Annual Cropland Changes over Large, Complex Agricultural Landscapes Using Google Earth Engine., , , , , , , and . Remote. Sens., 14 (19): 4896 (2022)A high-frequency mobile phone data collection approach for research in social-environmental systems: Applications in climate variability and food security in sub-Saharan Africa., , , , , , , , and . Environ. Model. Softw., (2019)