Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities

05/12/2017 ∙ by Anza Shakeel, et al. ∙ 0 ∙ share

Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. On the other hand satellite imagery captures scenes that are diverse. This paper describes a deep learning approach that analyzes a geo referenced satellite image and efficiently detects built structures in it. A Fully Convolution Network (FCN) is trained on low resolution Google earth satellite imagery in order to achieve end result. The detected built communities are then correlated with the vaccination activity that has furnished some useful statistics.

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Authors

Anza Shakeel
2 publications
Mohsen Ali
11 publications

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Using Satellite Imagery for Good: Detecting Communities in Desert and Mapping Vaccination Activities

Authors

Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition

Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic

Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico

Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features

Deep Learning for Recognizing Mobile Targets in Satellite Imagery

Landslide Geohazard Assessment With Convolutional Neural Networks Using Sentinel-2 Imagery Data

Using Deep Learning and Satellite Imagery to Quantify the Impact of the Built Environment on Neighborhood Crime Rates

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