Publication

Detection of Settlements in Tanzania and Mozambique by Many Regional Few-Shot Models

Résumé

In this work, we propose an approach to aid in mapping small settlements, which are often misclassified by models trained on a large-scale context (global or regional). We leverage pre-trained land cover models and few-shot learning to enhance the detection of these settlements. The backbone models are trained globally, but their application is localized through a spatial sampling strategy to address the challenge of detecting missed or unlabelled settlements. The proposed sampling strategy is based on the distance around a test patch and allows for the sampling of both backgrounds (non-settlements) points and settlements. Following this strategy results in a balanced dataset for model fine-tuning and ensures that the model is well-adapted to the local context. The idea is that nearby settlements share more similar properties, which is leveraged in our approach. We evaluate these transferred models by measuring the number of previously unmapped settlements detected by the fine-tuned classifier. For this, we manually annotated over two thousand buildings across two regions of Tanzania, previously unmapped in the original urban landcover product. Our results indicate the potential of the sampling approach, particularly when combined with a model pretrained with Momentum Contrast (MoCo). However, we also highlight the limitations in terms of spatial resolution of Sentinel-2 data for the detection of small settlements.

À propos de ce résultat
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.