Towards Consistent and Robust Slum Detection Using Multi-year Satellite Data

Recent advancements in computer vision have significantly improved slum detection using very-high-resolution satellite imagery. However, current algorithms are limited to identifying slums based on single-temporal labels and lack the capability to perform multi-temporal analysis. Here we present a supervised learning model trained on multi-temporal labels, specifically designed to maintain consistent performance across temporal variations. We evaluate our model against baseline approaches trained on singe-temporal labels. Case studies in two cities, Caracas and Karachi, demonstrate that incorporating additional temporal satellite imagery during training produces more consistent and reliable results for multi-temporal analysis. Our study suggests new ways to leverage temporal data in slum detection to effectively monitor urban poverty and track dynamics of informal settlements over time.