# AI-based evaluation of the SDGs: The case of crop detection with earth observation data

## TL;DR

This paper explores how AI and earth observation data can enhance SDG monitoring, focusing on crop detection using advanced machine learning models like U-net with SE blocks for satellite image segmentation.

## Contribution

It presents an overview of SDG targets measurable by AI, identifies key indicators, and demonstrates a novel application of U-net with SE blocks for crop detection in satellite imagery.

## Key findings

- U-net with SE blocks improves crop segmentation accuracy
- AI can effectively contribute to SDG monitoring
- Proposes a framework for AI-based SDG evaluation

## Abstract

The framework of the seventeen sustainable development goals is a challenge for developers and researchers applying artificial intelligence (AI). AI and earth observations (EO) can provide reliable and disaggregated data for better monitoring of the sustainable development goals (SDGs). In this paper, we present an overview of SDG targets, which can be effectively measured with AI tools. We identify indicators with the most significant contribution from the AI and EO and describe an application of state-of-the-art machine learning models to one of the indicators. We describe an application of U-net with SE blocks for efficient segmentation of satellite imagery for crop detection. Finally, we demonstrate how AI can be more effectively applied in solutions directly contributing towards specific SDGs and propose further research on an AI-based evaluative infrastructure for SDGs.