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Related Experiment Video

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Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

Urban tree species benchmark dataset for time series classification.

Clément Bressant¹, Romain Wenger¹, David Michéa²

¹LIVE UMR 7362 CNRS, University of Strasbourg, 3 rue de l'Argonne, Strasbourg, 67000, France.

|July 4, 2025

Summary

A new dataset enables urban tree species classification using satellite images and deep learning. This advances urban vegetation monitoring and climate resilience strategies.

Keywords:

Deep learning PlanetScope Sentinel-2 Time series classification Urban trees

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Area of Science:

Ecology
Remote Sensing
Computer Science

Background:

Urban tree classification is vital for ecological understanding and climate resilience.
Satellite image time series (SITS) offer a promising approach for urban vegetation monitoring.

Purpose of the Study:

To introduce a benchmark dataset for urban tree species classification using multi-source SITS.
To provide a reproducible framework for evaluating deep learning models and fusion strategies for urban vegetation analysis.

Main Methods:

A dataset was created using Sentinel-2 and PlanetScope imagery for Strasbourg, France.
It includes 45,084 trees of 20 species, formatted for time series classification.
Three InceptionTime-based deep learning models were trained and evaluated.

Main Results:

The dataset supports direct integration into deep learning frameworks.
Trained models achieved accurate species classification, with outputs including confidence scores and correctness flags.
An interactive t-SNE visualization aids in interpretability and error analysis.

Conclusions:

The presented dataset and framework advance urban vegetation monitoring capabilities.
This work facilitates the development of nature-based solutions for urban climate resilience.
It provides a valuable resource for researchers in remote sensing, urban ecology, and machine learning.