A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

2026-04-14

AI Summary

This study addresses the limited interpretability of deep learning–based species distribution models (SDMs), which, despite high predictive performance, often obscure underlying ecological mechanisms. For the first time, we integrate a concept-based explainable artificial intelligence (XAI) method—Robust TCAV—into SDMs, leveraging high-resolution multispectral and LiDAR drone imagery to construct the first open landscape-scale dataset explicitly aligned with ecological concepts. Using both convolutional neural networks (CNNs) and Vision Transformers, we not only validate model predictions against expert ecological knowledge but also uncover novel ecological associations. Our approach yields interpretable and actionable landscape-level insights that directly inform conservation decision-making. Both the code and the dataset are publicly released to foster reproducibility and further research.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Transparent, Interpretable, Explainable ML
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are the primary tools for this task, serving two purposes: achieving robust predictive performance while providing ecological insights into the driving factors of distribution. However, the increasing complexity of deep learning SDMs has made extracting these insights more challenging. To reconcile these objectives, we propose the first implementation of concept-based Explainable AI (XAI) for SDMs. We leverage the Robust TCAV (Testing with Concept Activation Vectors) methodology to quantify the influence of landscape concepts on model predictions. To enable this, we provide a new open-access landscape concept dataset derived from high-resolution multispectral and LiDAR drone imagery. It includes 653 patches across 15 distinct landscape concepts and 1,450 random reference patches, designed to suit a wide range of species. We demonstrate this approach through a case study of two aquatic insects, Plecoptera and Trichoptera, using two Convolutional Neural Networks and one Vision Transformer. Results show that concept-based XAI helps validate SDMs against expert knowledge while uncovering novel associations that generate new ecological hypotheses. Robust TCAV also provides landscape-level information, useful for policy-making and land management. Code and datasets are publicly available.

Problem

• Species Distribution Models
• Explainable AI
• Concept-based XAI
• Landscape Concepts
• Ecological Interpretability

Innovation

• Concept-Based XAI
• Species Distribution Models
• Robust TCAV
• High-Resolution Landscape Dataset
• Explainable AI