Temporal Poverty Prediction using Satellite Imagery

Islam, Mohammad Mohaiminul; Hasan, Mahmudul

dc.contributor.author	Islam, Mohammad Mohaiminul
dc.contributor.author	Hasan, Mahmudul
dc.date.accessioned	2020-11-11T09:16:24Z
dc.date.available	2020-11-11T09:16:24Z
dc.date.issued	2019-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/669
dc.description	Supervised by Dr. Md. Hasanul Kabir Professor, Department of Computer Science and Engineering Islamic University of Technology (IUT)	en_US
dc.description.abstract	Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. Our goal is to extract large-scale socioeconomic indicators from highresolution satellite imagery.We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh	en_US
dc.title	Temporal Poverty Prediction using Satellite Imagery	en_US
dc.type	Thesis	en_US