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Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping., , , , и . AAAI, стр. 3929-3935. AAAI Press, (2016)Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning., , , , , и . CoRR, (2017)Twice Is Nice: The Benefits of Two Ground Measures for Evaluating the Accuracy of Satellite-Based Sustainability Estimates., , , и . Remote. Sens., 13 (16): 3160 (2021)Predicting Livelihood Indicators from Crowdsourced Street Level Images., , , , , , и . CoRR, (2020)Generating Interpretable Poverty Maps using Object Detection in Satellite Images., , , , и . IJCAI, стр. 4410-4416. ijcai.org, (2020)Special track on AI for CompSust and Human well-being.Using machine learning to assess the livelihood impact of electricity access., , , , и . Nat., 611 (7936): 491-495 (2022)Semantic Segmentation of Crop Type in Africa: A Novel Dataset and Analysis of Deep Learning Methods., , , , , и . CVPR Workshops, стр. 75-82. Computer Vision Foundation / IEEE, (2019)Efficient Poverty Mapping from High Resolution Remote Sensing Images., , , , , и . AAAI, стр. 12-20. AAAI Press, (2021)SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning., , , , , , , , , и . NeurIPS Datasets and Benchmarks, (2021)Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery., , , , , , , , и . AIES, стр. 353-359. ACM, (2019)