IAMAP: Unlocking Deep Learning in QGIS for non-coders and limited computing resources

TL;DR

IAMAP is a user-friendly QGIS plugin that democratizes deep learning for remote sensing by enabling non-coders and those with limited resources to extract features, perform clustering, and validate models using advanced self-supervised learning techniques.

Contribution

It introduces a flexible, easy-to-use QGIS plugin that leverages recent self-supervised learning advances, reducing the need for large datasets and high computing power.

Findings

Enables feature extraction with various deep learning architectures.

Supports dimensionality reduction, clustering, and similarity mapping.

Facilitates model calibration and validation without extensive coding or resources.

Abstract

Remote sensing has entered a new era with the rapid development of artificial intelligence approaches. However, the implementation of deep learning has largely remained restricted to specialists and has been impractical because it often requires (i) large reference datasets for model training and validation; (ii) substantial computing resources; and (iii) strong coding skills. Here, we introduce IAMAP, a user-friendly QGIS plugin that addresses these three challenges in an easy yet flexible way. IAMAP builds on recent advancements in self-supervised learning strategies, which now provide robust feature extractors, often referred to as foundation models. These generalist models can often be reliably used in few-shot or zero-shot scenarios (i.e., with little to no fine-tuning). IAMAP's interface allows users to streamline several key steps in remote sensing image analysis: (i) extracting image features using a wide range of deep learning architectures; (ii) reducing dimensionality with built-in algorithms; (iii) performing clustering on features or their reduced representations; (iv) generating feature similarity maps; and (v) calibrating and validating supervised machine learning models for prediction. By enabling non-AI specialists to leverage the high-quality features provided by recent deep learning approaches without requiring GPU capacity or extensive reference datasets, IAMAP contributes to the democratization of computationally efficient and energy-conscious deep learning methods.