# Urban trees species classification using Sentinel-2 and Planetscope satellite image time series

Marie Latil, Romain Wenger, David Michéa, Germain Forestier, Anne Puissant

► To cite this version:

Marie Latil, Romain Wenger, David Michéa, Germain Forestier, Anne Puissant. Urban trees species classification using Sentinel-2 and Planetscope satellite image time series. 2025 Joint Urban Remote Sensing Event (JURSE), May 2025, Tunis, France. pp.1-4, 10.1109/JURSE60372.2025.11076039. hal-05350772

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Distributed under a Creative Commons Attribution 4.0 International License
Urban trees species classification using Sentinel-2
and Planetscope satellite image time series

Marie Latil

Romain Wenger

David Michéa

Germain Forestier

Anne Puissant

LIVE UMR 7362 CNRS

LIVE UMR 7362 CNRS

EOST UAR 830 CNRS

IRIMAS UR 7499

LIVE UMR 7362 CNRS

Univ. of Strasbourg

Univ. of Strasbourg

Univ. of Strasbourg

Univ. of Haute-Alsace

Univ. of Strasbourg

Strasbourg, France

Strasbourg, France

Strasbourg, France

Mulhouse, France

Strasbourg, France

mlatil@unistra.fr

r.wenger@unistra.fr

michea@unistra.fr

germain.forestier@uha.fr anne.puissant@unistra.fr

**Abstract**—Urban trees play an important role in the city's thermal comfort, carbon storage capacity, rainwater infiltration, capture of some pollutants or to enhance biodiversity. The classification of tree species in urban areas is essential for monitoring their health and distribution, particularly in the context of increasingly frequent heatwaves and global warming. In this study, we present a deep learning-based approach to classify the 20 most representative urban tree species in Strasbourg using Satellite Image Time Series (SITS) from Sentinel-2 and PlanetScope. We implement and compare three state-of-the-art models used for Time Series Classification (TSC) — InceptionTime, H-InceptionTime, and LITE — augmented with sensor fusion (features fusion) to exploit the complementary spectral and temporal information from both sensors. Our results show that the sensor fusion approach significantly improves classification accuracy, with the best scores for H-InceptionTime model with an accuracy of 69%. Also, the error can be explained by the specific spatial organisation of the trees in urban areas, where dense and heterogenous tree distribution inside parks is more prone to errors than aligned ones.

**Index Terms**—urban tree species, SITS, Sentinel-2, PlanetScope, deep learning

I. INTRODUCTION

Since the pre-industrial area in 1850-1900s, the world average temperature has soared. According to the Intergovernmental Panel on Climate Change (IPCC), +1.5°C compared to the pre-industrial area is the tipping point beyond which the consequences of climate change will become irreversible on human society and ecosystems [1]. This point is the IPCC projection of global warming by 2030. However, according to the Copernicus observations, the global average temperature from July 2023 to June 2024 has reached the highest record so far with +1.64°C above the pre-industrial average. On top of this, new records are broken each year.

In this context of climate change, urban areas' temperatures are increasing and creating urban heat islands [2]. This has consequences on the population's thermal comfort and well-being. In addition, the United Nations is expecting 68% of the world's population to live in urban areas by 2050, as a lower bound. To face this issue, vegetation is an urgent priority to act on. Indeed, not only are urban greenspaces able to balance the side effects of urbanisation and rising temperatures, but vegetation including trees is fundamental to protect biodiversity as an ecosystem services provider [3]. Climate change is

impacting trees, their health and their capacity to survive the fast changes happening, therefore an advanced knowledge of tree species and their location is necessary. They need to adapt themselves to global warming and changing climates.

Thanks to technological development, satellite images are an essential tool for studying various subjects. Providing images with a regular frequency, it is possible to access the tree's time series, a widely used resource for studying urban trees. However, many research works are currently using expensive imagery such as hyper-spectral imagery, aerial or active remote sensing like LiDAR. These approaches are expensive and so not suitable with an overall aim of generalisation on large territories. On the contrary, a part of the satellite-provided images are free and use high to very high spatial resolution, especially Sentinel-2 and PlanetScope.

In many research works, machine learning have produced relevant results but with many user expertise. With the free and massive access to Satellite Image time series (SITS) with high or very high spatial resolution, deep learning approach have proliferated for instance for forest management or landcover map production. Recently, SITS combined with deep learning methods has shown greater efficiency and promising results than classic machine learning to monitor urban vegetation [4].

A previous study using machine learning (Random Forest) and three types of sensors (PlanetScope, Sentinel-2, and Pleiades) achieved an accuracy of 73% for classifying the 10 most represented urban tree species in Strasbourg, France [5]. Building on this, our work applies deep learning models adapted to multivariate Time Series Classification (TSC) to classify the 20 most representative urban tree species in Strasbourg using freely available satellite imagery. Each model incorporates feature fusion to leverage the unique characteristics of each sensor.

The main contributions of this work are:

* A new application of deep learning for urban tree species classification using multivariate TSC,

* A sensor fusion approach combining spectral and temporal data from multiple satellite sources,

* A comprehensive evaluation of three state-of-the-art TSC models with real-world satellite data
## II. AREA OF INTEREST AND DATASETS

The study covered the metropolitan area of Strasbourg, located in northeastern France close to Germany. The urban trees inventory is freely directly provided by the city (Patrimoine arboré 2022 ©OpenDataStrasbourg). Among the 85,000 trees, the 20 most representative species represent 50% of the entire dataset.

Two sensor images have been used for this study. The first ones are coming from the Sentinel-2 mission thanks to the European Space Agency's Copernicus program. Images are freely available every five days and contain 13 spectral bands from the blue to the short-infrared spectrum with a spatial resolution from 10 to 60 meters. The 10 spectral bands of 10 and 20-meter resolutions are kept on the tile T32UMU covering the area of interest. Throughout the year 2022 in Strasbourg, 22 cloud-free images were available. These images have been coregistered using the processing chain carried out on the EOST-A2S (Application Satellite Survey) HPC facility of University of Strasbourg, part of the Data Terra Research Infrastructure [6].

The second source of images is the PlanetScope imagery handled by Planet Labs. The daily images have a spatial resolution of 3.125 meters. The Ortho Tile correction provided by PlanetScope has been applied to the images. The 4-band product has been used for this study and comprises 53 cloud-free images over 2022 in Strasbourg. Both datasets were processed to reflect top-of-atmosphere correction levels. A reference image from June was co-registered onto a Sentinel-2 image from June (at the vegetation peak), then all the other PlanetScope images were co-registered onto the referenced one.

The dataset has been created by computing the zonal statistics of each tree. A buffer of 1m was applied around each tree and the median has been computed to avoid the outliers values which can happen with a buffer overlapping pixels. Previous tests showed that there were no differences in the results by using theoretical crowns, segmentation or 1-meter size buffers [5].

## III. METHODS

### A. InceptionTime

The Inception model was originally made for image classification, but the adaptation for TSC has shown high results. The model is composed of 6 Inception modules, each one computing convolutions in parallel with different filter lengths to capture time series features at multiple scales. According to [7], the longer the filters are, the better the model's performances will be, with the condition of having enough training data to avoid overfitting. Each Inception module also has a bottleneck convolutional layer to reduce the dimension and computational cost. To keep the same dimension after every convolution layer, a zero-padding is applied.

### B. H-InceptionTime

The H-InceptionTime (Hybrid InceptionTime) architecture has been developed to capture specific patterns in time series

[8]. The main idea is to define non-trainable hand-crafted filters to catch 3 specific patterns in time series: increase, decrease, and peaks. These patterns are extremely relevant in the case of tree classification in the study, because tree phenologies have a flowering period in spring (increase), usually a peak before a leaf fall (decrease).

### C. LITE: Light Inception with booting techniques for Time Series Classification

The LITE model has been developed recently with the aim of reducing the number of parameters while maintaining performance. The LITE model is 2.78 times faster than InceptionTime, while consuming 2.79 times less CO₂ and Power with only 2.34% of InceptionTime's number of parameters [9]. This is therefore interesting to try this new model made for TSC. In addition to the bottleneck convolutions which are known from the InceptionTime model to reduce the number of parameters, the LITE model also adds DepthWise Separable Convolutions (DWSC), which uses fewer multiplications for a smaller number of parameters to learn compared to standard convolutions.

<figure><img src="image_2.png" /><figcaption>Fig. 1. Sensor fusion architecture for InceptionTime model. Same architecture for H-InceptionTime and LITE fusion.</figcaption></figure>

### D. Sensors fusion

The 2 sensors Sentinel-2 and PlanetScope are complementary, both in spectral and temporal diversity. The first is richer in spectral bands, while the second is richer in the number of dates. Features fusion is a way to describe better the tree species phenology. While machine learning is making data fusion before the model or probabilities fusion after it, deep learning has the advantage of improving the learning process by making a fusion of the features during the process [10]. The fusion is done at the end of the architecture, just after the average pooling. Two models are running in parallel, one for each sensor, and only the fully connected layer is done using both sensors' outputs to predict the class (Figure 1).

### E. Implementation details

The dataset has been split randomly into training, validation and test sets by respecting the class distribution. The cross-validation has been applied using the K-fold method made
to avoid overfitting by driving the learning across the entire dataset. The training and validation sets have been divided into K=5 folds. During each of the 5 splits, one different fold is used as the validation one, the rest is for the training. The number of epochs has been fixed empirically to 30 (and 60 for LITE) based on the loss curves. Besides, the Adam optimizer has been used [10] with an initial learning rate $\epsilon = 0.001$ and a plateau reduction of patience 5 with a factor of 0.1. Finally, the class weights have been assigned as the inverse of their frequency [10]. This method is forcing the network to pay more attention to the under-represented classes thanks to higher weights. All the models have been rewritten in PyTorch (originally made in Keras)<fnref n="1" />.

## IV. RESULTS

### A. Global results assessment

First, Table I highlights the mean accuracy from the 5 splits for each model and its standard deviation. The best results are happening with sensor fusion. The H-InceptionTime model has the best accuracy with 69% of correct classifications, followed closely by the InceptionTime model with 67%. The others have lower accuracies, especially LITE with 45%. Besides, the sensor fusion is always improving the results, and it is particularly efficient for LITE which gained around 10% of accuracy with both sensors and went over the 50% mark. We achieved the same accuracy for the classification of 20 tree species as [5] reported for only 10 species.

<table><caption>TABLE I
ACCURACY FOR SCENARIOS ON 3 CONVOLUTIONAL MODELS (INCEPTION FOR INCEPTIONTIME, H-INCEP FOR H-INCEPTIONTIME AND LITE).</caption><thead><tr><th></th><th>S2</th><th>Planet</th><th>S2-Planet</th></tr></thead><tbody><tr><td>Inception</td><td>0.6292 +/- 0.0043</td><td>0.6221 +/- 0.0131</td><td>0.6768 +/- 0.0139</td></tr><tr><td>H-Incep</td><td>0.6305 +/- 0.0037</td><td>0.6242 +/- 0.0103</td><td><b>0.6913 +/- 0.0034</b></td></tr><tr><td>LITE</td><td>0.4574 +/- 0.0244</td><td>0.4875 +/- 0.0251</td><td>0.5612 +/- 0.025</td></tr></tbody></table>

### B. Analysis per tree species

The F1-scores have been compared for each species for H-InceptionTime and InceptionTime models in Figure 2 using both sensors. The two models are competitive, but all the species except 2 (Pinus nigra and Alnus glutinosa) have a higher F1-score for the H-InceptionTime model. Besides, the *Platanus x acerifolia* has from far the highest F1-score in both models, and it is the most represented species. It is also noticeable that the least represented species do not have the lowest F1-scores, e.g. *Styphnolobium japonicum* has the second highest score and is the 4th species with the fewest number of trees.

<figure><img src="image_3.png" /><figcaption>Fig. 2. F1-score comparison for H-Inception and InceptionTime models per species</figcaption></figure>

### C. Qualitative analysis

The process results can be observed on a map. The aim is to understand if the geographical arrangement of trees has an influence on the model prediction, as well as their spacing. Figure 3 maps the urban trees in Strasbourg for the H-InceptionTime model and highlights the correct and wrong classifications. There are visually more correct than wrong predictions, which is consistent with the average accuracy. Two focuses on various areas are shown on the right side. The trees in the streets near Republic Place are well-aligned and more separated, and the predictions are particularly accurate. On the contrary, the trees in the Orangerie Park are closer to each other, not especially aligned and the classifications are weaker.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Qualitative results for the test set over Strasbourg for H-Inception model. On the right side of the map, two focuses over the Orangerie park and the Place de la République</figcaption></figure>

## V. DISCUSSION

The results showed that a sensor fusion is improving the accuracy for all the convolutional models. Indeed, the two

<footnote n="1">¹https://github.com/latilmarie/Ubran-tree-classification</footnote>
sensors complement each other in terms of spectral bands and number of dates. Besides, the H-InceptionTime model furnishes the best results even if the gap with classic Inception-Time is small. It confirms the hypothesis that the hand-crafted features can detect up and down slopes and peaks as explained in [8].

On the other hand, LITE has the lowest accuracies in general. Indeed, this model has been created with the aim of reducing the number of parameters while maintaining the results. However, the classification task is very complex. It could explain why this model does not have the best results, although it exceeds 50% of accuracy with both sensors.

In Figure 2, almost all the species have higher F1-scores for the H-InceptionTime model, which is consistent with the fact that the H-InceptionTime model is the best one in accuracy. This model is improving the general results but not significantly compared to InceptionTime. The most representative species *Platanus x acerifolia* has the highest F1-score for both models because the network has learned intensively about this species' features [11]. Indeed, it has more than 7500 trees in the original dataset, while the second species has around 4500 trees, way less. However, the fact that the least represented species do not have the lowest F1-score proves that the imbalance between the classes has been corrected efficiently with the weights assigned inversely to the frequency [12].

Moreover, the map of urban trees in Strasbourg in Figure 3 has good classifications in the streets near Republic Place because the trees are aligned and separated. First, the space between them allows the pixels to have pure values and not a combination with neighbour trees. Besides, the tree species in these streets are almost all the same. On the other hand, there are more wrong classifications in the Orangerie Park. In fact, in this case, the trees are close to each other, which can create a blend of values for each tree and confuse the neural network. In addition, the species in this park are very different from one tree to its neighbour. It might be easier for the network to classify well-spaced trees. In addition, the general environment can influence the behaviour of the phenology of the neighbour tree [13].

## VI. CONCLUSION AND PERSPECTIVES

Through this research, the purpose was to implement new deep learning models adapted to multimodal and multivariate satellite time series. Methodological issues have been focused on sensor fusion. The 3 convolutional models developed have been efficiently applied and improved results. The key findings and the main contributions of this research are the very high and competitive results of the H-InceptionTime model through the hand-crafted filters improving detection of growth, decline, and peaks. The InceptionTime model remains competitive, while the LITE has for now lower results. Also, we have shown that the error is higher in parks where urban trees are dense and low in streets where the trees are perfectly aligned and spaced. In the future, it could be useful to analyse the errors, by looking at the tree phenology and by examining the tree

spatial organisation and localisation. For instance, producing a Grad-CAM could help to analyse the tree phenology, while analysing the Local Climate Zones (LCZ) could be convenient for the spatial organisation. All these perspectives are related to the climate stakes and the need to act on several sides, including urban vegetation, which is at the core of ecosystem services.

## ACKNOWLEDGMENT

We want to thank Ali El Hadi Ismail Fawaz<fnref n="2" /> for our discussions and his code review during this project. Thanks to the Open Data Strasbourg.eu for the topographic database of urban trees (*Patrimoine Arboré*). We also would like to thank the computer center Mesocentre for providing the calculation resources. This research has been funded by the CNES-TOSCA project AIM-CEE (2019-2022) and is part of the french ANR project M2-BDA (ANR-24-CE23-1130).

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[13] M. Franco, "The Influence of Neighbours on the Growth of Modular Organisms with an Example from Trees," *Phil. Trans. R. Soc. Lond. B*, vol. 313, no. 1159, pp. 209–225, Aug. 1986.

<footnote n="2">²https://hadifawaz1999.github.io/</footnote>