-
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Millions of people worldwide are absent from their country's census. Acc...
read it
-
Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information
Access to accurate, granular, and up-to-date poverty data is essential f...
read it
-
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data
Informal settlements are home to the most socially and economically vuln...
read it
-
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
In the globalized economic world, it has become important to understand ...
read it
-
Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
This work explores the combination of free cloud computing, free open-so...
read it
-
Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen
Armed conflict has led to an unprecedented number of internally displace...
read it
Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis
Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history. Non-government organizations and local government units are faced with the challenge of identifying, assessing, and monitoring rapidly growing migrant communities in order to provide urgent humanitarian aid. However, with many of these displaced populations living in informal settlements areas across the country, locating migrant settlements across large territories can be a major challenge. To address this problem, we propose a novel approach for rapidly and cost-effectively locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time-series satellite imagery. We demonstrate the effectiveness of the approach in identifying potential Venezuelan migrant settlements in Colombia that have emerged between 2015 to 2020. Finally, we emphasize the importance of post-classification verification and present a two-step validation approach consisting of (1) remote validation using Google Earth and (2) on-the-ground validation through the Premise App, a mobile crowdsourcing platform.
READ FULL TEXT
Comments
There are no comments yet.