# Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale

arXiv: https://arxiv.org/abs/1704.02965 · HTML: https://mlsanity.com/paper/1704.02965/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2017.

## Built on by (most cited first, 23 of 23)

- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector](https://mlsanity.com/paper/2112.01723/) (2021)
- [Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?](https://mlsanity.com/paper/2303.04204/) (2023)
- [A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities](https://mlsanity.com/paper/2308.00465/) (2023)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)
- [Persona-aware and Explainable Bikeability Assessment: A Vision-Language Model Approach](https://mlsanity.com/paper/2601.03534/) (2026)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Explainable, automated urban interventions to improve pedestrian and vehicle safety](https://mlsanity.com/paper/2110.11672/) (2021)
- [Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India](https://mlsanity.com/paper/2402.13796/) (2024)
- [Predicting Poverty Level from Satellite Imagery using Deep Neural Networks](https://mlsanity.com/paper/2112.00011/) (2021)
- [Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery](https://mlsanity.com/paper/2304.09874/) (2023)
- [Climate-sensitive Urban Planning through Optimization of Tree Placements](https://mlsanity.com/paper/2310.05691/) (2023)
- [GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM](https://mlsanity.com/paper/2507.13323/) (2025)
- [Digital-to-Physical Transfer of Adversarial Patches for Aerial Vehicle Detection](https://mlsanity.com/paper/2606.00159/) (2026)
- [Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches](https://mlsanity.com/paper/2201.12693/) (2022)
- [Automatic satellite building construction monitoring](https://mlsanity.com/paper/2209.15084/) (2022)
- [Event detection from novel data sources: Leveraging satellite imagery alongside GPS traces](https://mlsanity.com/paper/2401.10890/) (2024)
- [Probabilistic Image-Driven Traffic Modeling via Remote Sensing](https://mlsanity.com/paper/2403.05521/) (2024)
- [CrossVIT-augmented Geospatial-Intelligence Visualization System for Tracking Economic Development Dynamics](https://mlsanity.com/paper/2412.10474/) (2024)
- [VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond](https://mlsanity.com/paper/2505.22353/) (2025)
- [The changing surface of the world's roads](https://mlsanity.com/paper/2512.04092/) (2025)
- [UrbanWell: Benchmarking Multimodal Large Language Models for Spatio-Temporal Urban Wellbeing Analytics](https://mlsanity.com/paper/2606.15890/) (2026)
- [CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training](https://mlsanity.com/paper/2607.07292/) (2026)

## Abstract (arXiv)

Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of 20 land use classes across ~300 European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.