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

This zenodo repository contains a multimodal, high-resolution dataset of 2,103 patches (653 concept-specific and 1,450 random) extracted from multispectral and LiDAR drone data. It is designed to support concept-based XAI (e.g., TCAV) for modeling species distribution at fine-scale.

Dataset Structure

The dataset is organized by concept class. Each directory represents a specific landscape element (concept) and follows a standardized 3-modality structure:

├───[Concept_Name]
│   ├───image_patches  # 5-band multispectral data (B, G, R, RE, NIR)
│   ├───dsm_patches    # Digital Surface Model (Canopy elevation)
│   └───dtm_patches    # Digital Terrain Model (Ground elevation)

Concept Classes

• Vegetation: Hedge (Hedgerows), IsoTree (Isolated Trees), Wood (Woodlands).
• Agriculture: Cereal, Maize, Wheat, PermG (Permanent Grassland), TempG (Temporary Grassland).
• Farming Systems: Organic (Organic crops), Convent (Conventional crops).
• Water & Wetlands: LinW (Linear Water), SurfW (Surface Water), Wet (Wetlands).
• Infrastructure: Build (Buildings), Road (Roads).
• Baseline: random_images (1,450 randomly sampled background patches).

Study Sites and Acquisition

The data were acquired in April 2024 using a Trinity F90+ drone equipped with MicaSense Dual MX and Qube240 sensors. To ensure a robust representation of diverse agricultural landscapes, data were collected across five heterogeneous study sites in France, ranging from extensive dairy farming systems to highly intensive cropping systems.