# Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

arXiv: https://arxiv.org/abs/1510.00098 · HTML: https://mlsanity.com/paper/1510.00098/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2015.

## Built on by (most cited first, 30 of 37)

- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [Extending the WILDS Benchmark for Unsupervised Adaptation](https://mlsanity.com/paper/2112.05090/) (2021)
- [Continental-Scale Building Detection from High Resolution Satellite Imagery](https://mlsanity.com/paper/2107.12283/) (2021)
- [Active Statistical Inference](https://mlsanity.com/paper/2403.03208/) (2024)
- [A Data-Based Perspective on Transfer Learning](https://mlsanity.com/paper/2207.05739/) (2022)
- [When does Bias Transfer in Transfer Learning?](https://mlsanity.com/paper/2207.02842/) (2022)
- [Bridging the Digital Divide: Performance Variation across Socio-Economic Factors in Vision-Language Models](https://mlsanity.com/paper/2311.05746/) (2023)
- [Gradual Domain Adaptation via Self-Training of Auxiliary Models](https://mlsanity.com/paper/2106.09890/) (2021)
- [Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping](https://mlsanity.com/paper/2105.02963/) (2021)
- [BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective](https://mlsanity.com/paper/2202.09541/) (2022)
- [Ask Your Distribution Shift if Pre-Training is Right for You](https://mlsanity.com/paper/2403.00194/) (2024)
- [Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping](https://mlsanity.com/paper/2108.07323/) (2021)
- [Robust Sampling for Active Statistical Inference](https://mlsanity.com/paper/2511.08991/) (2025)
- [A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty](https://mlsanity.com/paper/2406.02584/) (2024)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection](https://mlsanity.com/paper/2303.14322/) (2023)
- [Predicting Poverty Level from Satellite Imagery using Deep Neural Networks](https://mlsanity.com/paper/2112.00011/) (2021)
- [Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain](https://mlsanity.com/paper/2203.01068/) (2022)
- [Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning](https://mlsanity.com/paper/2205.14418/) (2022)
- [Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models](https://mlsanity.com/paper/2211.11797/) (2022)
- [Mitigating climate and health impact of small-scale kiln industry using multi-spectral classifier and deep learning](https://mlsanity.com/paper/2303.11654/) (2023)
- [Machine Learning for Economics Research: When What and How?](https://mlsanity.com/paper/2304.00086/) (2023)
- [Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks](https://mlsanity.com/paper/2312.00416/) (2023)
- [On the Adversarial Vulnerabilities of Transfer Learning in Remote Sensing](https://mlsanity.com/paper/2501.11462/) (2025)
- [Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research](https://mlsanity.com/paper/2501.14546/) (2025)
- [Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Images](https://mlsanity.com/paper/2504.08727/) (2025)
- [An Efficient Method for the Classification of Croplands in Scarce-Label Regions](https://mlsanity.com/paper/2103.09588/) (2021)
- [ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights](https://mlsanity.com/paper/2201.07612/) (2022)
- [A Topic Modeling Approach to Classifying Open Street Map Health Clinics and Schools in Sub-Saharan Africa](https://mlsanity.com/paper/2212.12084/) (2022)
- [Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia](https://mlsanity.com/paper/2302.14835/) (2023)

## Abstract (arXiv)

The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. 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. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). 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. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.