TY - JOUR AU - Ayush, Kumar AU - Uzkent, Burak AU - Tanmay, Kumar AU - Burke, Marshall AU - Lobell, David AU - Ermon, Stefano PY - 2021/05/18 Y2 - 2024/03/28 TI - Efficient Poverty Mapping from High Resolution Remote Sensing Images JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 35 IS - 1 SE - AAAI Technical Track on Application Domains DO - 10.1609/aaai.v35i1.16072 UR - https://ojs.aaai.org/index.php/AAAI/article/view/16072 SP - 12-20 AB - The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. This creates a substantial hurdle to the efficient scaling and widespread adoption of high-resolution-based approaches. To reduce acquisition costs while maintaining accuracy, we propose a reinforcement learning approach in which free low-resolution imagery is used to dynamically identify where to acquire costly high-resolution images, prior to performing a deep learning task on the high-resolution images. We apply this approach to the task of poverty prediction in Uganda, building on an earlier approach that used object detection to count objects and use these counts to predict poverty. Our approach exceeds previous performance benchmarks on this task while using 80% fewer high-resolution images, and could be useful in many domains that require high-resolution imagery. ER -