Work

Towards Streaming Land Use Classification of Images with Temporal Distribution Shifts

Year: 2025

Type: conference-paper

Abstract: In this study, we introduce a new pipeline that integrates Streaming Machine Learning (SML) models and the Momentum Contrastive Learning (MoCo) technique for the streaming classification of satellite images subject to temporal variations in distribution.We present preliminary results of an experimental campaign conducted on the Functional Map of the World-Time dataset, one of the first benchmarks specifically designed to address temporal distribution shifts in satellite imagery.The results demonstrate that the proposed pipeline enhances robustness and generalization over time, surpassing traditional strategies.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-166

Authors: Lorenzo Iovine, Giacomo Ziffer, Andrea Proia, Emanuele Della Valle

Institutions: Politecnico di Milano, Motorola (Italy)

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480571

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