FLAIR : a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
Authors: Anatol Garioud
Nicolas Gonthier
Loic Landrieu
Apolline De Wit
Marion Valette
Marc Poupée
Sebastien Giordano
Boris Wattrelos
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: Semantic segmentation
Satellite time series
Aerial imagery
Land cover
Geospatial Data
Multimodal learning
TL;DR: The FLAIR #2 dataset proposes an extensive collection of annotated high resolution images and satellite data time series for training and evaluating large-scale multimodal semantic segmentation methods.

Abstract: We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis. FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individually labeled pixels for precise land-cover classification. The dataset also integrates temporal and spectral data from optical satellite time series. 

FLAIR thus combines data with varying spatial, spectral, and temporal resolutions across over 817 km² of acquisitions representing the full landscape diversity of France. This diversity makes FLAIR a valuable resource for the development and evaluation of novel methods for large-scale land-cover semantic segmentation and raises significant challenges in terms of computer vision, data fusion, and geospatial analysis. We also provide powerful uni- and multi-sensor baseline models that can be employed to assess algorithm's performance and for downstream applications.

FLAIR:
a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery
Anatol Garioud
Nicolas Gonthier Loic Landrieu
Apolline De Wit
Marion Valette-
Marc Poupée"
Sébastien Giordano"
Boris Wattrelos'
French National Institute of Geographical and Forest Information (IGN)
Univ Gustave Eiffel, IGN, ENSG, LASTIG
3 LIGM, Ecole des Ponts, Univ Gustave Biffel, CNRS
National School of Geographic Sciences (ENSG)
(firstname. (asttname)@ggn....
Abstract
We introduce the French Land cover from Aerospace ImageRy (FLAIR), tensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides unique and rich resource for large-scale geospa- tial analysis. FLAIR contains high-resolution aerial imagery with ground sample distance of 20 em and over 20 billion individually labeled pixels for precise land- cover classification. The dataset also integrates temporal and spectral data from optical satellite time series. FLAIR thus combines data with varying spatial, spec- tral, and temporal resolutions across over 817 km2 of acquisitions representing the full landscape diversity of France. This diversity makes FLAIR a valuable resource for the development and evaluation of novel methods for large-scale land-cover semantic segmentation and raises significant challenges in terms of computer vision, data fusion, and geospatial analysis. We also provide power- ful uni- and multi-sensor baseline models that can be employed to assess algo- rithm's 's performance and for downstream applications. Through its extent and the quality of annotation, FLAIR aims to spur improvements in monitoring and understanding key anthropogenic development indicators such as urban growth, deforestation, and soil artificialization. Dataset and codes can be accessed httpss//ignt .gi wttchuu...oFAAA..
Context
According to a 2015 report by the Food and Agriculture Organization of the United Nations (FAO) [1], approximately 75% of the world's 's soils are in fair, poor, very poor condition. This degradation poses significant threats the health and long-term sustainability of ecosystems. Healthy soils provide invaluable ecosystem services, including: (i) providing natural habitats for numerous plant and animal species [2], (ii) acting as the largest carbon sink, surpassing the atmosphere and all combined biomass [3], and (iii) functioning as a rainwater reservoir, supporting food production and storing freshwater [4].
The degradation of soils and biodiversity is largely attributed to land artificialization [1]. which causes long-term damage the biological, hydrological, climatic, and agronomic functions of the soil due to its occupation or use [5, 6). In order to effectively monitor and manage land artificialization. public authorities have expressed the need for scalable land-cover monitoring tools. With the increasing availability of high- nigh-quality Earth Observation (EO) data, the French National Institute
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Figure 1: Detail from the FLAIR Dataset. The very high resolution annotation of 20 cm allows us to distinguish the exact extent of individual houses, roads, and trees.
of Geographical and Forest Information (IGN) [7] is exploring the use of artificial intelligence to automatically conduct high-resolution, global-scale land-cover mapping, an essential component for addressing soil degradation at national level [8]. A central aspect of this initiative is to create and disseminate precise and up-to-date reference datasets for researchers and policy-makers.
We introduce the French Land cover from Aerospace ImageRy (FLAIR) dataset, the largest multi- sensor land-cover dataset with very-high-resolution annotations. FLAIR combines very-high- resolution (VHR, 20cm) images, photogrammettr-deriveeee surface models, and optical Sentinel-2 multi-spectral satellite time series with nominal revisit time days at the equator. The diverse spatial, spectral, and temporal resolutions of these acquisitions offer valuable complementary perspec- tives for land cover analysis. Over 20 billion pixels have been hand-annotated by geospatial experts, using nomenclature of 19 land-cover classes. The data spans 817 km2 across 50 French sub-regions featuring diverse bioclimatic attributes at various times of the year, thus displaying complex and challenging domain shifts.
FLAIR combines data sources with heterogeneous spatial, temporal, and spectral resolutions and high- precision annotations, and aims to foster the development of new large-scale semantic segmentation methods. Given its scale and the complexity of the domain shifts it exhibits, FLAIR also presents an exciting challenge for the computer vision and machine learning communities.
2
Related Work
Numerous land-cover datasets have been introduced to train semantic segmentation methods. see Table 1. Existing datasets usually present a trade-off: they either offer high-resolution annotations but cover small extent (like Vaihingen [9]), or provide large-extent coverage but with low-resolution annotations (such as BigEarthNet [10] or SENI2MS [11]). In contrast, FLAAR offers both very high-resolution annotations (20 cm) while covering a large portion of the French territory.
FLAIR comprises over 20 billion individually, manually annotated pixels, which is over 1000 times more than SEN 12MS and 2 times more than BigEarthNet, which employs semi-automatic annotations. The DeepGlobe and LoveDA datasets, the closest counterparts our dataset, provide large coverage of 1717 km² at 50 em and 536 km2 at 30 em respectively. However, FLAIR provides over 3 times as many annotated pixels and higher resolution.
The spatial resolution of the annotation is crucial in land-cover analysis. Insufficient resolution prevents the precise measurements of surfaces and boundaries. Furthermore, small-scale features, such as individual houses, lone trees or roads, may not be captured accurately, limiting the potential applications of the derived segmentation.

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Table I: Land Cover Datasets. Publicly available datasets for semantic segmentation of land from remole sensing Earth observation imagery.
Annotation
Acquisition
Pixels 100
Resolution
Classes
Source
Resolution
Extent (km²)
Source
SAT-4/AT--6 [12]
0.9
28 m
4/6
semi-nuromatic (NLCD [13])
m
13 860k
acrial
SENI2MS [11]
100 m
17
lully-automatic (MODIS (14])
10
3551k
Sentinel- -
Vashingen
visual interpretation
cm
BuroSAT
110
10
BU Urhan Arlas
10
059
Sentinel-2
MultSennEE [17]
534
10 m
14
visual interpretation
10
57 433
Sentinel-1&&
Landeovemet [18]
589
10 m
7
semiiautomatic (MODIS [14)
10
58 982
Sentinel-2
Minif'rance [19]
1510
50 m
14
BU Urbun Atlas [16]
50 cm
53000
aerial
Sentinel-1&-2,
DynamicFarthNet
3
visual interpretation
16986
PlanetFusion acrial, UAV. satellite
OpenEarthMMap [21]
4
25-50 em
visual interpretation
25-50
Uwe-Billion-Pixel [22)
5 5000
4 m
24
visual interpretation
4 m
500000
Gaofen-2
LoveDA [23]
6 000
30 cm
visual interpretation
30 cm
536
aerial
Wordviiw-2233.
DeepGiobe [24]
6
50
visual interpretation
50
1717
GeoEEee-
BigliarthNet [10)
8 500
100
19
semii automatic (CLC (25))
10
850k
Sentinel 1& aerial,
FLAIR
20cm
visual interpretation
20 cm/10
Seutinel-2
Dataset Description
FLAIR combines granular pixel annotation with heterogeneous data sources across large and diverse spatio-temporal extent.
3.1 Extent & Annotation
Spatio-Tempores Distribution. The FLAIR dataset consists 762 patches represented in Figure 3. Each patch includes high-resolution aerial image of 0.2 m, yearly satellite image time series with a spatial resolution of 10 m, and pixel-precise elevation and land cover annotations at 0.2 resolution. As shown in Figure 5, the acquisitions are taken from 916 unique areas distributed across 50 French spatial domains (départements) covering approximately 817 km2 Aerial images were captured under favorable weather conditions between April and November from 2018 to 2021. Each satellite time series corresponds to the entire year acquisition of the matching aerial image.
Annotations. Each pixel has been manually annotated by phtoo--nterpreeattion of the 20 resolu- tion aerial imagery, carried out by a team supervised by geography experts from the IGN. During the annotation process, initially identified 18 classes. We group certain classes together due the rarity of certain classes, such as swimming pool, greenhouse, or snow, or potential ambiguity, as seen with ligneous and mixed vegetation. The resulting 12-class nomenclature leads more statistically robust evaluation metrics. Nonetheless, users can still access and use the extended nomenclature.
Table 2: Land-cover Class Distribution. Semantic nomenclature used by the FLAIR dataset and their proportion among the entire dataset.
Class
Class
Class
(1) building
7.1
(6) coniferous
4.3
(11) agricultural land
12.8
pervious
deciduous
(12) plowed land
impervious surface
12.1
brushwood
6.3
(13) other
(4) bare soil
3.1
(9) vineyard
3,0
(5) water
4.5
(10) herbuceous vegetaation
18.2
3

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Agricultural
0.15
Ierrbaceous
Deciduous
0.1
0.05
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Figure 2: Land Cover Spectral Dynamics. We represent the temporal progression of reflectance values for the red channel (B2) for different land cover types using Sentinel-2 satellite data. Each curve represents the mcan reflectance for land cover types over subset of the Ardèche department in 2020, with shaded regions indicating the standard deviation. This plot illustrates distinct spectral dynamics of different land cover types.
Movable objects like boats annotated according their underlying cover. Table 2 outlines the class set and their distribution. Refer to the appendix for more details.
Thanks to the high resolution of the aerial images, anthropic structures like roads and buildings can be identified with a high level of detail. Agricultural and natural lands like forests or herbaceous cover, which make up 65% of the dataset, often be challenging to distinguish from images alone. As shown in Figure 2, multispectral satellite time series prove to be particularly effective in characterizing the temporal evolution of plant phenology [26, 27], key motivation for incorporating these into the dataset.
Training Splits. The dataset is made up of 50 distinct spatial domains, aligned with the adminis- trative boundaries of the French départements. For our experiments, we designate 32 domains for training, 8 for validation, and reserve 10 the official test set (refer Figure or appendix). This arrangement ensures balanced distribution of semantic classes, radiometric attributes, bioclimatic conditions, and acquisition times across each set. Consequently, every split accurately reflects landscape diversity inherent to metropolitan France. It is important to mention that the patches come with meta-data permitting alternative splitting schemes, for example focused on domain shifts.
3.2 Acquisitions
FLAIR offers 3 complementary sources of acquisition, each with distinet nature and spa- tialsppettraltemmprrll resolutions: aerial images, elevation models, and satellite image time series.
Very High Resolution Aerial Images. The aerial images are taken from IGN's free license BD-Ortho product [28]. All aerial images are 512 x 512 in size with resolution of 20 cm per pixel, and feature 4 spectral channels: red, blue, green, and near-infrared. Each patch comes with metadata such the date and time of acquisition, geographical location and altitude of the patch centroid, and specifies about the camera used for acquisition. All images are aligned to a shared cartographic coordinate reference system (EPSG:2154), and radiometric corrections are applied to ensure homogeneity per spatial domain [29]. This means that colors should not be interpreted as physical measurements of channel reflectance.
Elevation. Each aerial image accompanied by an elevation value produced by the IGN. This information is not independent measurement but product derived from digital elevation model and digital surface model obtained through photogrammetry on the aerial images, thereby ensuring temporal consistency.
Sentinel-2 Time Series. Each patch is associated with satellite image time series from the Sentinel-2 constellation [30], as shown Figure 4. Fach image in the sequence of size 40 40 with 10 m pixel resolution, centered around the acrial image. Each pixel is characterized by 10

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Aerial
Satellite
Figure 3: Patches from FLAIR. The dataset is comprised of 77 762 patches. Each patch contains (i) 512 x 512 aerial image at 0.2m resolution with red, green, blue (RGB) and near-infrared (NIR) values, (ii) pixel-precise digital surface model providing elevation for each pixel, (iii) semantic labels for each pixel, and (iv) optical time series spatial dimension 40 and 10m per pixel, centered aerial image.
spectral bands, ranging from the visible to the medium infrared spectrum; additional details be found in the appendix. The time series span the entire year during which the corresponding aerial image was acquired and contain from 20 110 images, depending on the satellite availability and the orbit characteristics. We include acquisitions with cloud cover and provide cloud and snow probability masks obtained with Sen2cor [31] in the metadata, alongside information about the satellite and its orbit.
Only the spatial and temporal extents of the aerial images annotated. Terrain features that evolve over time, such as changing river banks or tidal patterns, may not be consistent throughout the time series. Nevertheless, the satellite time series provide invaluable spectral and temporal information, as well as a broader spatial context of 400 x 400m.
3.3 Specificities
The FLAIR dataset presents specificities often encountered in geospatial analysis but seldom in computer vision: large-scale multi-sensor acquisitions, and complex spatio-temporal domain shifts.
Multi-Sensor. FLAIR combines optical acquisitions with drastically different spatial (0.2m v.s. 10m), spectral v.s. 10 bands), and temporal (single-date w.s. year-long time series) resolutions. The discrepancy makes the task of integrating this diverse yet complementary information into a unified pixel representation substantial challenge.
The FLAIR dataset spans a large spatio-temporal extent across the entire French metropolitan territory and various seasons over years. This introduces complex prior-shift (for instance, there roof architecture across regions). This last phenomenon can profoundly impact the appearance of natural and agricultural vegetation, and may also affect the sunlight illumination conditions.
Domain-Shifts.
vineyards near Bordeaux than in Normandy), and concept-shift (variations in
Two camera models were used to capture the aerial images Vexcel's Ultracam Eage Mark3 [32] and IGN's CAMv2 [33]. Although generally minor, this can cause slight differences in resolution and

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00
000
00
2019-jen
2019-1eb
2019
2019-Agr
2019-May
2019:jum
219--uu
2019
2019-sep
2019-0xt
2019-Nov
2019-0ec
2020-jen
Sentinali2 acqulattion
Figure 4: Satellite Image Time Series. We represent a year-long satellite time series (top). and its associated mono-temporal aerial acquisition and corresponding annotations (bottom).
spectral sensitivity. Additionally, all aerial acquisitions undergo radiometric correction mitigate disparities caused by sunlight and contrast. This can lead to dissimilarities between spatial domains regarding the spectral response of identical materials.
IRAIN
VALIDATION
129
Spetial domains
Areas
Patches
Figure 5: Spatio-Temporal Distribution. Spatial units of the FLAIR dataset (left), and spatial and temporal distribution of the train validation / test split (right).
Baselines
We propose generic yet powerful multi-sensor architecture to serve as a baseline evaluate the semantic segmentation performance of different approaches.
General Architecture. An effective model for task needs capture both detailed textures from the aerial images and complex temporal dynamics from the time series. We propose a network architecture named U-T&T: U-net with Textural and Temporal information. As shown in Figure our model consists of two networks: one operating on high-resolution images with four radiometric channels (red, green, blue, infrared) and elevation channel, and one network operating on time series. Each network follows the state-of-the-ar approach for their respective data-source.
U-Net (spatial/texture branch): We U-Net [34] with ResNet34 backbone model [35] pre-trained on the ImageNet dataset [36]. We add two channels on the first layers to accommodate near-infrared and elevation pixel values. The weights of these two channels are initialized randomly [37]. This U-Net branch comprises approximately 24.4 million parameters.
U-TAE (spatio-temporal branch): employ U-Net with temporal attention (U-TAF) to process the Sentinel-2 imagery [38]. This model is specifically designed to extract multi-scale

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U-TAE temporal branch
sat. time series
Convolution
T=10x40x40
Segmentation
Temporal attention
U-TAE embedding
Grop Interpolate
Fusion Modula
annotation patch
... copy and concal.
down convolution
up convolution
5>512x512 1
LyGT
aerial patch
U-Net branch
Predicted patch
W5292
130550
JAS 1552A531
056644
Figure 6: The U-T&T model. Our architecture comprises two modules: (i) U-TAE network extracts spatio- temporal descriptors from the Sentinel-2 time series, and (ii) a U-Net network processes the aerial and elevation images. We merge both feature maps with branch fusion module. Both branches are supervised simultaneously with dedicated losses Lasia and Luu.
spatio-temporal feature maps from satellite image time series. The U-TAE branch includes approx- imately 2.9 million parameters.
Metadata Encoding. Metadata can significantly impact the interpretation of remote sensing ac- quisitions. To allow the network to model this specificity, we compute the following features: (i) spatial coordinates of the center of the patch (with Fourier features), (ii) altitude from sea level, (iii) year of acquisition (one-hot-encoded), and (iv) camera types (one-hot-encode)). These features are then processed with Multi-Layer Perceptron (MLP) and concatenated channelwise to the coarsest (innermost) feature map of the encoder of the U-Net branch. For more details, refer to the appendix.
Fusion Module. We propose fuse the multi-scale feature maps from the branches different scales. For each level of the U-Net encoder of width C and extent H x W, our proposed fusion module has two sub-parts:
This sub-module captures local spatio-temporal information from the image time series. We first crop the pixel-level feature map from the U-TAE to the extent of the acrial image. Then, we apply a spatial convolution with width of C' and interpolate the results to size H x W. Finally, we add the resulting tensor to the intermediate feature map of the U-Net encoder.
Cropped:
This sub-module captures larger spatial context of the patch from the image time series. We first compute the spatial mean for each channel of the output map of the U-TAE (of size 40 x 40). Then, we process the resulting vector with a three-layer MLP to map it to width C. We add this vector to each pixel of the intermediate feature map of the U-Net encoder.
Collapsed:
Network Supervision. The U-Net branch produces a prediction of size 13 x 512 x 512, which can be directly supervised pixelwise with the loss Cauraat chosen the categorical cross entropy. The output of the U-TAE branch is of size 13 x 40 x 40, and its extent is larger than the annotation. To adjust this, first crop it around the aerial image into tensor of size 13 10 x 10, then use bilinear interpolation to map it to the desired 13 x 512 x 512 dimension. This allows us to define its loss Cum the categorical cross entropy as well.
All class weights are set to except for the other class, which is set to 0 in both losses. The weight of the class plowed land is set to O in the loss Lus it corresponds transient state of land cover
7

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whose duration is much shorter than the yearly span of the time series. We combine both losses into single loss LT&T defined as their unweighted sum:
L Cacrial + Leat
The baseline implemented with Py Lightning [39]. The code for U-Net branch is taken from the segmentation gegmentation-models- PyTcrhh library [40], and the U-TAE network from its official repository [38]. We use the default U-TAE parameters, except for larger widths for the encoder and decoder
Implementation Details.
The network optimized with stochastic gradient descent, batch size of 10, and a learning rate of 0.001. We set the maximum number of epochs to 100 and use early stopping with patience of 30 epochs. We employ several augmentation strategies, such as cloud removal, temporal averaging, or geometrie augmentation; see the appendix for additional details. Our model is trained on cluster of 12 NVIDIA Tesla V100 GPUs with 32 GB memory.
5 Benchmark
Metric. We assess the performance of different configurations of our baseline using the mean intersection-over-uiii (mloU) the first 12 classes, excluding the other class. We train each model times from scratch, allowing us to compute the standard deviation of the performance.
Analysis. We report in Table the performance for several variations of our baseline models. U-TAE model using only the satellite image time series and whose prediction is upsampled to the resolution of the aerial images leads to much lower performance than its high-resolution counterparts. In contrast, the performance of the multi-sensor U-T&T model is comparable to the simple U-net operating on high-resolution images: 54.7 v.s. 54.9. However, when using appropriate augmentation strategies for both, the performance gap widens: 54.9 to 56.9. Specifically, evaluated the following strategies:
cloud cover of 60% according to the meta-data (with a probability threshold 0.5). This led to a gain of +0.3 mloU point on average.
FILT. We remove satellite images with
AVG M. We compute the monthly average of the cloudless satellite acquisitions, ensuring that the time series have at most elements. While the gain is modest (+0.1 point), this approach decreased the memory usage and the length discrepancy between locations.
MTD. Our meta-data encoding strategy did not show any impact on either approach.
AUG. We perform geometric augmentations on the aerial images: flip, resize, and random affine transform. This resulted in +0.6 point increase in performance.
Table 4 provides per-class loU scores for the two best runs of U-Net and U-T&T. We report better results for the U-T&T model for all 12 classes. In particular, we observe significant improvements for classes that particularly benefit from temporal information and broader spatial context, such as bare soil, coniferous, and vineyard classes. Conversely, the improvement is smaller for the water and plowed land classes. These classes can change significantly across the year due to tides, shifting river beds, or harvesting events. Consequently, year-long observation may not bring useful information. See Figure 7 for qualitative illustrations. More examples are provided in the appendices.
Decoder Architecture. We evaluated the performance of two other decoder networks aerial image branch: FPN [41] and Deepl [42], while we keep the ResNet34 encoder backbone. We report Table 5 the results with and without our proposed enhancement strategies. The largest network, DeepLabV3, slightly outperforms the other architecture by 0.4 mloU points.

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Table 3: Quantitative Evaluation. Performance of the U-Net and multi-sensor U architectures the test set. Results are averages runs each configuration. PARA.: number parameters of the network, in million; EP: best validation loss epoch.
INPUT
FILT.
AVG
MID
AUG
PARA.
EP
mloU
UTAB
Sit x
2.3
16
36.1
+FILT +AVG
sat
2.3
18
36.91:022
U-Net
aerial
24.4
54.71:0.1
+MTD +AUG
acrial
/
/
24.4
55.2::0.1 52
U-T&T
ucrial+sat
x
x
x
27.3
04.9:10.7 9
serial+sat
27.3
55.2.1:
+AVG
aerial+sat
x
27.3
10
55.0:10.7
+MID
acrial+sat
x
x
X
27.3
7
54.9-10.66
+AUG
acrial+sat
27.3
22
55.5++
+ILT +AVG +MT +AUG
scrial+sat
v
v
27.3
21
16.9-11.1
Table 4: Per- Cla-Ssass Evaluation.
We report the classwise ToU for the best run of the U-Net baseline (acrial imagery), and the U-T&T baseline (aerial and satellite imagery).
building
impare.
Dare soil
conferous
desiduous
broshword
vineyards
herbaccouus
plowed
best
model
U-Net
54.7
80.1
47.3
69.9
30.8
79.9
57.6
70.1
23.9
60.1
46.5
54.5
35.8
UT&&
58.6
83.5
52.0
74.0
43.7
82.2
64.2
73.7
25.6
64.6
47.17
55.2
37.0
Table 5: Decoder Architecture. We report the performance of different network architcctures, with and without our proposed enhancement strategies.
Aerial imagery branch model
enhancements
PARA
mioU
U-Net [34]
27.3
54.9110.7
FPN [41]
26.1
15.5+110
DeepLabbV3 [42]
U.Net [34]
+FILT +AVG M +MTD +AUG
27.3
$6.911111
FPN
+FILT +AVG +MTD +AUG
26.1
56.2#0.6
DeepLabV3 [42]
+FILT +AVG +MTD +AUG
$7.340.9
Discussion
Challenges. The FLAIR dataset used in two CodaLab [43] scientific challenges, both of which received over 1000 submissions, indicating significant interest. The first challenge, from November 2022 March 2023, involved domain adaptation while the second challenge, from May to September 2023, incorporated Sentinel-2 time series and introduced a new test dataset. These challenges allowed the scientific community leverage our extensive labeling effort evaluate, design, and improve large-scale semantic segmentation methods for multi-sensor Earth observation.
Limitations. The FLAIR dataset is limited to metropolitan France. Although France's 's territory is quite diverse, featuring oceanic, continental, Mediterranean, and mountainous bioclimatic regions, it does not contain tropical or desert area. As a national agency, IGN's focus limited to France. However, similar efforts by other countries agencies would increase the diversity of available data and stimulate the design and evaluation of geographically robust methods.
The FLAIR dataset's 'ss reliance purely optical data may limit the applicability of the models trained on it to regions with pervasive cloud cover. Incorporating synthetic aperture radar time series, such as Sentinel-1 time series, may address this limitation and could be considered in future extension.
Quality Control. As the annotations are made through visual interpretation, some errors are unavoidable, especially for classes that are visually hard distinguish, such as bare soil and pervious surfaces. We manually annotated around 37k randomly selected polygons, hidden from the annotating

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Aerial image
(b) True labels
prediction
U-Net prediction
Figure 7: Qualitative illustration. Black ellipses shows areas that are significantly better classified by multi-sensor approach U-T&T than the standard U-Net model.
teams. This covered total area of 18.7 km², equivalent to approximately 0.75% of the entire dataset, or 468 million pixels. If any batch of annotations did not meet a set accuracy criterion 95%, it was rejected and returned for re-annotation. This iterative process fostered productive exchanges between annotators and the geography experts from IGN, thereby ensuring high-quality dataset.
Ethics. Releasing a large-scale. high-resolution land-cover dataset openly could raise potential concerns related to privacy, security, and possible misuse. Indeed, detailed information about private properties could be extracted, possibly aiding illegal activities. However, both aerial and satellite images are already publicly available, and we only provide visual interpretations. Furthermore, high-risk areas such as military facilities and nuclear plants are explicitly excluded from the dataset.
FLAIR focuses the French metropolitan area, which does not well represent countries in arid tropical climates. This bias could steer scientific efforts towards developing models that perform well in the Western hemisphere while overlooking developing countries. These countries could significantly benefit from automated land-cover tools due to their challenging climates or struggling institutions. IGN is committed equity and plans future work focus on more diverse climates, for instance, by planning acquisitions and annotations of French overseas territories.
Potential Social Impact. The primary goal of FLAIR is to stimulate the development of robust and scalable tools for automatic land cover. These tools could be instrumental in monitoring and curbing soil artificialization and its catastrophic environmental impacts. Our dataset can also be used to develop and assess the performance of other key geosaptial analysis tasks such as deforestation or sea level monitoring.
By providing a curated and accessible dataset of Earth observations, we also hope to draw the interest of the computer vision community to the challenges of geospatial analysis and contribute to establishing remote sensing data as a standard modality for evaluating machine learning algorithms' performance. The dataset also facilitate the pre-training of models for other geospatial analysis tasks, such as object detection, super-resolution, or change detection.
Licence
FLAIR is under the Open Licence 2.0 of Etalab. This licence has been designed to be compatible with any free licence that at least requires an acknowledgement of authorship. and specifically with the previous version this licence as well as with the following licences: United Kingdom's 's "Open Govermment Licence" (OGL), Creative Commons' "Creative Commons Attribution" (CC-BY) and Open Knowledge Foundation's "Open Data Commons Auributioon" (ODC-BY).
Acknowledgment
The experiments conducted in this study were performed using HPC/AI resources provided by GENCI-IDRIS (Grant 0022-A0131013800 This work was supported by the European Union through the project "Copernicus / FPCUP" as well as by the French Space Agency (CNES) and Connect by CNES. The authors would like to acknowledge the valuable support and resources provided by these organizations.
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A Appendix
A.1 Benefits of the multi-source approach
Sentinel-2 imagery are synergistic approach with VHR acrial images for land cover mapping, as cach source has unique advantage allowing to distinguishing nuanced semantic classes, a critical need in detailed geospatial analysis. Some of the main benefits of integrating Sentinel-2 are:
Increased spectral resolution: Unlike aerial acquisitions that generally contain only four spectral bands (with single one in the infrared), Sentinel-2 is furnished with a 10-band multispectral imager. This includes bands in the near-infrared spectrum, which prove essential for discerning vegetation phenology [44].
Multi-temporal resolution: Sentinel-2 provides consistent yearly time series. This capability allows our model to trace the temporal progression of each pixel's spectral response, proving invaluable in distinguishing between similar plant species, as depicted in Figure 2. As an illustrative example, while an "agricultural land" and a "herbaceous surface" might appear identical during specific times (exhibiting low herbaceous vegetation), the agricultural land remains barren of vegetation during other parts of the year. VHR aerial acquisitions, in contrast, are limited to single-date images.
The coarser spatial resolution of Sentinel-2 (10 m) compared to aerial images (20 cm) provides an unexpected advantage. By offering broader con- text, Sentinel-2 enables our model to harness wider receptive fields. Consequently, each 102x102m aerial patch is linked with Sentinel-2 image time series spanning a 400x400m area.
Larger spatial context:
The Sentinel-2 time series benefits from consistent spectral calibra- tion, which aids in countering the radiometric inconsistencies introduced during the BD Ortho's 's correction process.
Spectral Consistency:
A.2 Sentinel-2 Time Series
Table 6 indicates the original bands acquired by the Sentinel-2 satellites and considered in the FLAIR dataset. The images were downloaded from the Sinergise API [45] as Level-2A products (Bottom-O0 Of the-Atmosphere reflectances) which are atmospherically corrected using the Sen2Cor algorithm [31]. Sentinel-2 sensor acquires images at 10, 20 and 60 spatial resolutions. The 60 m bands mainly intended for atmospheric corrections are not taken into account and the 20 bands are resampled during data retrieval to 10 m by the nearest interpolation method.
Table 6: Sentinel-2 spatial and spectral resolutions. Original spatial and spectral resolutions of Sentinel-2 images along with the correspondence between original band number and the distributed data.
Original Band number
Central wavelength (om)
Bandwidth (nm)
Original Spatial resolation (m)
FLAIR Sparial resolution (m)
band number
2
490
65
10
10
3
2
560
35
10
10
4
3
665
30
10
10
5
4
705
20
10
5
10
7
6
783
20
20
10
8
7
842
115
10
10
Sa
8
865
20
20
10
11
9
1610
90
20
10
12
10
2190
180
20
10
Table 7 indicates the cloud & snow probability masks provided as separate files alongside the Sentinel- 2 acquisitions. It should be noted that cloud detection in satellite images is a complex task because
More advanced algorithms [46] could be beneficial.
14

---

of the diversity of clouds (thin, scattered clouds). As result, probability masks can contain errors, notably confusion with surfaces with high albedo and close the top of cloud, as is the case with the roofs of industrial buildings.
Table 7: Provided cloud and snow masks.
Mask
FLAIR band nomber
Original Spatial resolution (m)
FLAIR Spatial resolation (m)
Snow probubility (SNW)
20
10
Cloud probubility (CLD)
20
10
Table 8 provides information about the number of dates included the filtered Sentinel-2 time series for the train and test datasets. On average, each area is acquired on 55 dates over the course of year by satellite imagery.
Table 8: Sentinel-2 Time series length. Number of acquisitions (dates) the Sentinel-2 times series of one year (corresponding to the year of acrial imagery acquisition).
aequisitions super area
Sentinel-2 time series (1 year)
min
max
macan
train dataset
20
100
55
lest dataset
114
Note that cloudy dates not suppressed from the time series. Instead, the masks provided and can be used to filter the cloudy dates if needed.
The spatial size of Sentinel-2 time series has been empirically determined and set to 40. Nevertheless, we provided this dataset wider areas than the 40 x 40 used for our baseline. However, there is a limit of 110 pixels for edge patches. The choice of time series spatial size has an impact the spatial context provided to both the U-TAE and U-Net branches through the collapsed fusion sub-module [47].
A.3 Semantic classes
Overall semantic class number of pixels and frequency of the FLAAR dataset are provided in Table 9, The class distribution in percentages of the train and test sets are presented in Figure 8. The detailed description of the original semantic classes is provided in Table 10.
The ground truth labels based on photo-interpretation of the aerial imagery 20 and has been manually produced by experts following a call for tenders from the IGN. An initial spatial multi-level image segmentation approach using PYRAM [48] was applied, simplifying the labeling at the small cluster level. This segmentation was modified interactively when deemed appropriate.
Train class frequency
Test class frequency
Plaett
600
5.9%
0.4
5.17%
2.74%
0.65%
5.00%
1,0%
A815
3.55%
Foowed appicaooral Aeed
Dnricational lood
Figure 8: Class distribution of the train dataset (left) and test dataset (right).
15

---

Table 9: Details about the semantic classes of the main nomenclature of the FLAIR dataset and their corresponding label values, frequency in pixels and percentage among the entire dataset.
Class
Label Value
building
1,453,245,093
7.13
pervious surface
2
1,495,168,513
7.35
impervious surface
3
2,467.133,374
12.1
bare soil
4
629,187,886
3.09
5
922,004,548
4.52
coniferous
873,397,479
4.28
dociduous
7
3,531,567,944
17.32
brushwood
8
1,284,640,813
6.3
vineyard
9
612,965,642
3.01
herbaceous vegetation
10
3,717.682,095
18.24
agricultural land
11
2,541,274,397
12:47
plowed land
12
703.518.642
3.45
other
>13
153,055,302
0.75
Table 10: Semantic classes of the FLAIR dataset.
Class description
Note: as previously stated, semantic classes assigned cluster level, given aerial image, only observable objects are labeled, whereby temporal aspects are not token into consideration.
Anthropized surfaces without vegetation (1, 2, 13 and 18)
Class - building includes not only huildings but also other types of constructions such as towers, agricultural silos, water towers and damss Greenhouses (class are an exception.
Class 2- pervious surface defined as man-made hare snils cnvered with mineral materials (e.g. gravel, loose stones) and considered to be
pervious, It includes pervious transport networks (e.g. gravel pathways, milways), quarries, landfills, building sites coastal ripraps.
Class 3 impervious surface is defined as man-made bare soils that impervious due their building materials (e.g. concrete, asphalt. cubblesiones). includes rondways, parking lols, and certain lypes of sports fields.
Class 13 swimming pool defined as made artificial (open-air) swimming pools. It included in class 5 (water).
Class 18 28--00 greenhouse although it can be considered as building, is given a distinct lahel. Greenhouses are a class of their own and are not part of class
Natural areas without vegetation (4, and 14)
Closs bare soil defined natural permanently bare soil.. These natural soils without vegetation throughout the year gencrally are covered with sand, pebbles, rocks or stones. Examples of natural hare soils are frequently found in coastal, mountainous and forested areas. Class 5- water defined as arcas covered by water, such sea, rivers, lakes and ponds. An exception are swimming pools (class 13). extremely rare class as the images are taken the summertime and oaly few
Class 14 refers to surfaces covered by
regions in France are covered with snow year year-round. round.
Woody natural vegetation surfaces (6, 8, 15, 16 and 17)
Class 6- coniferour, is defined as trees identitaalle as comierous (pines, lirs, cedars, cypress trees, ...) and taller than 5 m.
Class 7 deciduous is defined as trees identifiable as deciduous (aaks, beeches, hirches, chestnuts, poplars, and taller than
Class brushwood refers to natural woody surfaces with a vegetation less than 5 m high. It includes short and young trees, brushwood, shrublands, mountain moors and abandoned agricultural lands.
Class 15 clear-cut, is defined as forest areas, in which the trees have been cut down and harvested.
Class 16- ligneous is an extremely rare class used to deseribe forest arcas with a homogeneous representation of either coniferous or deciduous trees.
Class 17 mixed is an extremely rure class used to describe forest areas with beterageneous trees for which the typex of trees (comiferous/ deciduous) he determined with sufficient certainty.
Agricultural surfaces (9, 11 12)
Class vineyard despite being an agricultural the land, assigned class apart, charncteristies.
being their rather distinctive land
Class agricultural land cacompasses various different agricultural classes. For example, besides major crops, also includes permancnt and temporary grasslands with agricultural use. Vineyards (class 9) are not included in this class.
12 - plowed land defined as agricultural land with visible vegetation (e.g. recenily plowed and freshly harvested land).
Herbaceous surfaces (10)
Clas herbuceous regelation delines herbaceous surfaces are intensively explotted agriculture purposes. This class
omaamnntal lawas (e.g. gardens, public parks), recreational fields (e.g. used for sport), natural herbacoou areas in forested or mountainous areas. mon-cultivated grass in agricultural areas or along transportaioon networks.
16

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A.4 Aerial imagery and spatial domains
Within spatial domain, all acrial acquisitions are radiometrically corrected to reduce disparities in sunlight and contrast. Nonetheless, this homogenization is not applied equally across all the different spatial domains can be seen in Figure 9. As opposed to satellite imagery, the pixel intensity in the image channels can therefore not be considered as physical measure.
Figure 9: Radiometric diserepancies of the aerial images between domains. The 3 channels image displayed is composite of Infrarnfrreed, Red and Green spectral information.
A.5
Benchmark architecture
A.5.1 U-Net (spatial/texture branch)
We choose U-Net architecture [34] with a ResNet34 encoder backbone (pre-trained on the ImageNet dataset [36]) for a total of 24.4 M parameters and rely on the implementation available in patpreetttiioommmdllsssppiie
library [40] and trained with the PyTTrrch lightning [39] framework. The architocture employed is illustrated Figure 10.
IMG input
PRED cusput
728
16
5125512
EISPEAS
192
384
1111
convv 3x3, batch norm.. ReLU, conv 3x3
MTD Input
BRC. In Aire
copy
down-stride 2x2
up-oonv pool 2x2
eTC. ONE Aize
ResNet residuals potential metadata integretion
Figure U-Net architecture used the baseline. IMG input image: MTD imput metadata: PRED prediction output. One potential and traditional appreach to integrate the metadata would be to add a Multi-layer Perceptron for encoding and add the output to the output of the last layer of the encoder an additional band the IMG input.
17

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Concerning the exploitation of metadata, simple approach has been tested [49]. The strategies explored have first step of metadata encoding: positional encoding of spatial and temporal infor- mation and one-hot-encoding for camera type and aerial image acquisition year. A shallow MI. with dropout (probability of 0.4) and ReI.U activation then defined to jointly encode the metadata and to provide a specified output size. Subsequently, multiple different integration strategies with the current ResNet34/U-Net segmentation architecture are possible. We have chosen a commonly employed strategy (depicted as "bootto" in Figure 10) consists in matching the MLP output size to the output size of the last layer the ResNet34 encoder. The two vectors (encoded metadata and encoded images) can then be added and fed into the first layer of the architecture's 's decoder. Strategies following similar approaches that add the MLP encoded output at different positions in the architecture's 's encoder or decoder parts (e.g.. after the first input convolution layer, with the last decoder layer, or even added as a sixth channel to the input image) are possible. A positional encoding of size 32 is used specifically for encoding the geographical location information.
The exploitation of metadata deserves to be studied more by the computer vision community, as could bring real gains by taking advantage of the specificity of remote sensing data.
A.5.2 Fusion module of the U-T&T model
A Fusion Module is employed within the U-T&T baseline model to integrate the feature maps from satellite time-series (with broader spatial extent) into the feature maps from the aerial imagery branch. The details of this module can be seen in Figure 11. Within the Fusion Module, two sub-modules (cropped and collapsed) have different purposes and focus on distinct aspects: the spatio-temporal information and the spatial context. This Fusion Module is applied to match with each feature maps of the U-Net encoder.
U-TAE embedding
64x40x40
Cropped
Collapsed
CenterCrop
mean (12,1)-)
64 x 10 10
64
Conv2d (64,out, k-1) out 10 10
Linear (64)
Dropaut (0.4)
ReLU
Interpolate (bilinear)
Linear
Dropout (0.4)
ReLU
Linear (out)
outx1
view repeat W
Fusion module
out H W
Figure 11: Fusion module. This module takes as input the last U-TAE embeddings. It applicd to each stage of the U-Net encoder feature maps. out corresponds to the channel size of the U-Net encoder feature map and H and W to the corresponding spatial dimensions.
A.5.3 Data augmentation
By introducing variance in the dataset, image data augmentation helps to prevent overfitting and provides trained models with enhanced generalization capabilities.
For our baseline, only geometric transformations are explored using the Albumentation library. Vertical and horizontal flips, and random rotations of 0, 90, 180 or 270 degrees are tested, A data augmentation probability of 0.5 is used.
18

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A.6
Benchmark results
A.6.1
Official data split of the FLAIR dataset
The following per domain split of the data has been used for the experiments:
TRAIN:
D006, DO07, DO08, D009, D013, D016, D017, D021, D023, D030, D032, D033, D034, D035, D038, D041, D044, D046, D049, D051, D052, D055, D060, D063,
D070, D072, D074, D078, DO80, DO81, D086, D091
VALIDATION:
D004, D014, D029, D031, D058, D066, D067, DO77
TEST:
D015, D022, D026, D036, D061, D064, D068, DO69, D071, DO84
A.6.2 Extra results
We present in Table 11 the performance of a U-TAE network using only satellite image time series upsampled spatially to the resolution of the aerial images. The lower resolution this sensor leads to signficantly worse results.
Table 11: Quantitative Evaluation. Performance of the U-Net and multi-sensor T&T architectures the test set. Results are averages runs each configuration. PARA.: number parameters of the network, million; EP: best validation loss epoch.
INPUT
FILT.
AVG M.
MID
AUG
PARA.
EP
mloU
U-Net
acrial
X
24.4
54.7:100.1
+MTD
actial
V
x
24.4
54.7:t0.2
+MTD +AUG
acrial
v
24.4
55.24+0.1
U-TAE
x
x
2.3
36.1+:0.3
35.9
+FILT +AVG M
v
v
2.3
18
36.9 1.02
L-T&T
54.9.10.7
+FILT
uerial+sal
v
x
8
8
27.3
11
55.2:1114
+MTD
acrial+sat
x
x
x
27.3
54.9:10.6
55.5:111.5
+FILT +AYG M +MID +AUG
actial+sat
6 27.3
21
56.9-t1.11
Figure 12 illustrates the confusion matrix of the best U model. This confusion matrix is derived by combining all individual confusion matrices per patch and is normalized by rows. The analysis of the confusion matrix shows that the best U-T&T model achieves accurate predictions with minimal confusion in the majority of classes. However, when it comes to natural areas such as hare soil and brushwood, although there is improvement due to the use of Sentinel-2 time series data, certain level of uncertainty remains. These classes exhibit some confusion with semantically similar classes, indicating the challenge of accurately distinguishing them.
More qualitative examples can be found in Figure
19

---

building
0.92
0.01
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
pervious surface
0.01
0.77
0.11
0.02
0.01
0.00
0.01
0.00
0.00
0.05
0.01
0.00
impervious surface
0.02
0.07
0.88
0.00
0.00
0.00
0.01
0.00
0.00
0.02
0.00
0.00
bare
0.00
0.20
0.01
0.60
0.15
0.00
0.00
0.02
0.00
0.02
0.00
0.00
water
0.00
0.00
0.01
0.01
0.97
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Actual
coniferous
0.00
0.00
0.00
0.00
0.00
0.77
0.16
0.04
0.00
0.02
0.00
0.00
deciduous
0.00
0.00
0.00
0.00
0.00
0.06
0.84
0.04
0.01
0.03
0.01
0.00
brushwood
0.00 0.02
0.01
0.02
0.01
0.03
0.16
0.46
0.00
0.24
0.03
0.00
vineyard
0.00 0.01 0.00
0.00
0.00
0.00
0.00
0.00
0.87
0.04
0.05
0.03
herbaceous vegetation
0.00
0.03 0.01
0.02
0.01
0.01
0.04
0.04
0.00
0.61
0.22
0.00
agricutural land
0.00
0.00
0.00
0.00
0.01
0.01
0.17
0.72
0.05
plowed land
0.00
0.05
0.00
0.00
0.00
0.00
0.02
0.00
0.01
0.02
0.29
0.59
building
pervious
surfacc
impervious
bare
soil
water
coniferous
deciduous
brushwoed
vineyard
herbaceous
vegetation
plowed
surface
agriccnn land
Predicted
Figure 12: U-T&T model confusion matrix of test dataset. The matrix normalized by rows.
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Aerial image
True labels
U-T&T prediction
U-Net prediction
Figure Illustration patch-wise results. Random results on FLAIR dataset the multi-sensor approach U-T&T than the standard U-Net model.
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B Datasheet for FLAIR dataset
B.1 Motivation
Q1 For what purpose was the dataset created? Was there specific task in mind? Was there particular gap that needed to be filled? Please provide description.
The FLAIR dataset is created train and evaluate models that can predict very-high- resolution land cover maps from diverse data sources with heterogeneous spatial, temporal, and spectral resolutions. The main gap we are addressing is the lack of large-scale data with high-definition annotations.
Q2 Who created the dataset (e.g,, which team, research group) and on behalf of which entity (e.g., company, institution, organization)?
This dataset is presented by the French National Institute of Geographical and Forest In- formation (IGN), French public state administrative establishment aiming to produce and maintain geographical information for France. The IGN has the mission to docu- ment and measure land-cover on French territory and provides referential geographical datasets, including very-high-resolution
aerial images and topographic maps. IGN produces reference data and carries out innovation, research and teaching activities. As part of its innovation activities, the IGN provides the FLAIR dataset to democratize access large-scale open powerful machine learning models through the research and development of open resources.
Q3
Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number.
The funding the FLAIR dataset is 100% public. This work was sponsored by the Ministry of Ecological Transition (more specifically the Directorate for Planning, Housing and Nature Direction générale de I l'aménngement, du logement et de nature) and the Fund for the transformation of public action (Fonds pour la transformation de I' l'action publique) from the Minister of the Civil Service. The IGN is funded by the French Ministry of Ecological Transition and the French Ministry of Agriculture.
Q4 Any other comments?
[N/A]
B.2
Composition
Q5 What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?
We provide aerial image with corresponding land segmentation along with Sentinel-2 satellite image time series around each aerial patch. The acquisitions taken from 916 unique areas distributed across 50 French spatial domains (départements), covering approximately 817 km? The test labels will be released the end of the second challenge hosted on CodaLab. We made our baseline codes openly available on the FLAIR GitHub page (https: // htt///ggtttttbt com/ IIN/// FLAIR- 2- AI- Challenge).
Q6 How many instances are there total (of each type, if appropriate)?
We provide 77,762 triplet acrial image, Sentinel-2 time and land cover segmentation. 11888,841,722 annotated pixels at a spatial reso-
The FLAIR dataset encompasses lution 0.20 m from aerial imagery with a 19 classes land cover. For each area, a comprehensive one-year record of Sentinel-2 acquisitions is also provided. A further overview the statistics may be seen in the following annexes.
Q7 Does the dataset contain all possible instances or is it sample (not necessarily random) instances from a larger set?
The FLAIR dataset, derived from a larger dataset obtained by IGN for cartographic production upon the request of the French government, serves as a representative sample encompassing approximately one-third of the available data. While the complete dataset covers 64 spatial domains, the FLAIR dataset focuses 50 domains by excluding
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contiguous domains and intra- domain areas. Nevertheless, the selected 50 domains offer comprehensive representation terms of land cover classes, acquisition dates, and macro-climates, and encompass the metadata associated with the entire dataset. The expertise of IGN was leveraged to ensure the selection of dataset that is representative and informative.
Q8 What data does each instance consist of?
Each instance consists of an aerial image. Each image is 512 x 512 in size with a resolution of 20 cm per pixel, and feature 4 spectral channels: red, blue, green, and near- -ear-infrrrrd along with an elevation value as fifth channel. Each patch is associated with satellite image time series from the Sentinel-2 constellation (Drusch al., 2012) of size 40 x 40 with 10 m pixel resolution, centered around the aerial image. Each pixel from the Sentinel-2 sequences is characterized by 10 spectral bands.
Q9 Is there a label or target associated with each instance?
[Yes] We provide complete pixel-precise land cover segmentation per image (19
classes).
Q10 Is any information missing from individual instances?
[No]
Q11 Are relationships between individual instances made explicit (e.g., users' movie ratings, social network links)?
[No]
Q12 Are there recommended data splits (e.g., training, development/validationn, testing)?
Yes, we provide data splits for reproducing the results of the baselines. The test split has been explicitly selected to address the complex domain shifts of geospatial data.
Q13 Are there any errors, sources of noise, or redundancies in the dataset?
As the annotations are made through visual interpretation with quality control, some errors are unavoidable, especially for classes that are visually hard to distinguish. Internal quality control with multiple annotations has been performed to limit such errors. There are no redundancies in the dataset, each image covers distinct area.
Q14 Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)?
This dataset is self- containet and will be stored and distributed by the IGN, public institute. The dataset is under the Open Licence 2.0 of Etalab.
Q15 Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor-patient confidentiality, data that includes the content of individuals' non-public communications??
[No] The building class does not contain information that would not be available in other open-access sources, such as the cadaster. We have specifically avoided high-risk areas such as military installations or nuclear plants.
Q16 Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.
[No]
Q17 Does the dataset relate to people?
The dataset may feature pedestrian or individuals, but the resolution of 20cm/pixel and the aerial perspective not sufficient to recognize them uniquely.
Q18 Does the dataset identify any subpopulations (e.g., by age, gender)?
[No]
Q19 Is possible to identify individuals (i.c., one or more natural persons), either directly indirectly (i.e., in combination with other data) from the dataset?
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[No]. The resolution of 20cm/pixel and the aerial perspective is insufficient to recognize them uniquely.
Q20 Does the dataset contain data that might considered sensitive in any way (e-g.. data that reveals racial ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such social security numbers; criminal history)?
[No]
Q21
Any other comments? [No]
B.3
Collection Process
Q22
How was the data associated with each instance acquired?
The aerial images are sampled from the ORTHO HR imagery collection. is a mosaic of all the individual images taken during an aerial survey done by IGN and mapped onto cartographic coordinate reference system. The individual images projected to the RGE * DTM, which provides solely the altitude of the ground.
The Sentinel-2 time series were downloaded from the Sinergise Sentinel-Hub API as Level-2A products (see annexes for more information).
Q23 What mechanisms or procedures were used collect the data (e-g., hardware apparatus or sensor, manual human curation, software program, software API)?
The IGN selected several acquisition companies through call for tender with strict specifications.
Q24 If the dataset is a sample from a larger set, what was the sampling strategy (e.g.. deterministic, probabilistic with specific sampling probabilities)?
The sampling strategy involved class frequency, acquisition dates distribution, radio- metric histogram analysis and geographical location spread. The final sampling based on these comprehensive variables was made manually by experts at the IGN.
Q25 Who was involved in the data collection process (e.g,, students, crowdworkers, contrac- tors) and how were they compensated (e.g., how much were crowdworkers paid)?
IGN contracted geography experts from the private sector selected through public call for tender to annotate the dataset. The quality control of the dataset was carried out by geography experts affiliated with IGN. The creation of the dataset was facilitated by researchers and developers employed by IGN under their work contracts.
Q26 Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)?
The collection of aerial imagery spanned from 2018 to 2021, which coincides with the duration required for an aerial survey to encompass the entirety the French territory. Annotations were then applied to the aerial images, aligning with the same time frame. Subsequently, the dataset was created in 2022 after the final processing for both the aerial imagery and annotations.
Q27 Were any ethical review processes conducted (c.g., by an institutional review board)?
[No]
Q28 Does the dataset relate to people?
[No]
Q29 Did you collect the data from the individuals in question directly, or obtain it via third parties or other sources (e.g., websites)?
[N/A]
Q30 Were the individuals in question notified about the data collection?
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[N/A]
Q31 Did the individuals in question consent to the collection and use of their data?
[N/A]
Q32 If consent was obtained, were the consenting individuals provided with mechanism to revoke their consent in the future or for certain uses?
[N/A]
Q33 Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., data protection impact analysis) been conducted?
[No]
Q34 Any other comments?
[No]
B.4
Preprocessing, Cleaning, and/or Labeling
Q35 Was any prepooesingddeeiigglllelinni the data done (e-g., discretization or bucket- ing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)?
[No]
Q36 Was the "raw" data saved in addition to the preprocesedddemmeellbbeedd data (e.g.. to support unanticipated future uses)? If so, please provide link other access point to the "raw"" data
[No]
Q37
Is the software used to preprocess/clean/lael the instances available?
[No]
Q38
Any other comments?
[No]
B.5
Uses
Q39 Has the dataset been used for any tasks already?
The optical images of FLAIR train split were used for two data challenges ran in 2022 and 2023 by IGN.
Marsocci et al., 2023 used a subset of FLAIR for to evaluate techniques for unsupervised domain adaptation.
Q40 Is there a repository that links any or all papers or systems that use the dataset?
[Yes]. We propose below list of scientific publications and systems that use FLAIR dataset:
Garioud et al., 2022 provides technical description of the FLAIR aerial imagery
dataset;
Garioud al., 2023 provides insight on the multi-sensor fusion of acrial and
satellite imagery:
Marsocci et al., 2023 experiments remote sensing unsupervised domain adaptation using geographical coordinates a subset of the FLAIR dataset.
Q41 What (other) tasks could the dataset be used for?
We encourage future researchers to use FLAIR dataset for several tasks. Particularly, see applications in land cover segmentation and multi-sensor fusion. Due to the breadth of the data, also offers unique opportunity for pre-training of models for other geospatial analysis tasks with low resource, such as object detection, super-resolution, or change detection.
Q42 Is there anything about the composition of the dataset or the way was collected and preproceseed/cleeneellle that might impact future uses?
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This dataset is geographically limited to metropolitan France. Although France's 's terri- tory quite diverse. featuring oceanic, continental, Mediterranean, and mountainous bioclimatic regions, it does not contain tropical or desert areas.
The FLAIR dataset's reliance purely optical data may limit the applicability of the models trained on it to regions with pervasive cloud cover.
Q43 Are there tasks for which the dataset should not be used?
[No]-
Q44 Any other comments?
[No]_
B.6
Distribution
Q45 Will the dataset be distributed to third parties outside of the entity (e-g., company, institution, organization) on behalf of which the dataset was created?
[Yes] the dataset will be open-source.
Q46 How will the dataset be distributed (e.g., tarball on website, API, GitHub)?
The data will be available through .iip files available on the FLAIR project page hosted on GitHub (https: /tttpp::/igntt //gnfff.f.. .igggttthhh Reaebiio/FFR /oFLAIRR))
Q47 When will the dataset be distributed?
All data with the exception of the test split presently accessible by registering for an ongoing challenge hosted on Codalab. The entire dataset, including the test split, will be released under an open-source license the FLAIR project page in early October 2023.
Q48 Will the dataset be distributed under copyright other intellectual property (IP) license, and/or under applicable terms use (ToU)? If so, please describe this license and/or ToU, and provide link other access point to, otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions.
[Yes] The data is governed by the Open Licence 2.0 of Etalab lattps://wwk. 1ttp////////oenlieeeeee ooe.e/iennnccceeeeeeeeeeeee .ddf). wwwpooneeeetttt////eeeeeeeeeeeee
etalab. gouv
Q49 Have any third parties imposed IP-based or other restrictions on the data associated with the instances?
[No]
Q50 Do any export controls or other regulatory restrictions apply to the dataset or to individual instances?
[No]
Q51
Any other comments?
[No]
B.7
Maintenance
Q52
Who will be supprringgnstingiaanniiinng the dataset?
IGN will support hosting of the dataset and metadata.
Q53
How can the owner/curator/maaager of the dataset be contacted (e.g., email address)?
ai- cha Lii-chhllegggeinn.. fr
Q54 there an erratum?
[No] There no erratum for our initial release. Errata will be documented as future
releases on the dataset website.
QS5 Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)?
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Additional modalities (e.g., supplementary satellite, aerial, UAV-based imagery) may be added to the FLAIR dataset.
Q56 If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)?
N/A
Q57 Will older versions of the dataset continue to be suppotee/hste/mmaittainee?? [Yes] We are dedicated to providing ongoing support for the FLAIR dataset.
Q58 If others want to extend/angment/huild on/contribute to the dataset, is there mecha- nism for them to do so?
Proposed extensions or corrections to the FLAIR dataset may be submitted to the providers for consideration. The IGN will assess the feasibility of incorporating the suggested modifications, considering factors such as data licensing, maintenance requirements, and relevance.
Q59 Any other comments?
[No].
27


Reviews:
[{"review_id": "N9C6KNzDYa", "reviewer": "Reviewer_g3Jt", "summary": "", "questions": "", "limitations": "The dataset is limited to French territory, and the Sentinel-2 multi-spectral data is not cloud-cover filtered.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper is cleanly organized, written in plain English to the point, and supported by figures and tables summarizing the relevant aspects of the work.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "2asJyq0Z2h", "reviewer": "Reviewer_pqJp", "summary": "", "questions": "", "limitations": "The authors have adequately discussed the limitations. It is also good to consider multimodalities and dataset shifts in the dataset.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper is well-written, with clear data specifications. The dataset is solid and collected in a rigorous way. The paper also included a good comparison with popular existing data sets such as MultiSenGE, MiniFrance, OpenEarthMap and BigEarthNet.\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "mIHCYAcy8h", "reviewer": "Reviewer_uZv1", "summary": "", "questions": "", "limitations": "The authors discuss the primary limitation which is that this dataset is limited to French regions and therefore lacks some of the geography and vegetative diversity that would be found in other regions.  It would be useful to quantify how well this data transfers to an entirely unseen domain.  \n\nThe baselines rely on the 20cm high resolution data, which has not been discussed as to how it was collected.  Therefore it is unclear how this could be extended to other areas.  ", "rating": 1, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "Significance: Most remote sensing datasets are of very low resolution from public satellite (10m resolution) or extremely high but narrowly defined datasets (e.g. drones of 1-2 fields at 2cm).  As satellite capabilities are rapidly improving and is becoming available at 5-20cm, this data is particularly interesting.  \n\nRelevance: Remote sensing is an important area within the broader research community, but is also an additionally important domain to study as most algorithms are currently designed for natural scenes imagery and the challenges of remote sensing are often ignored.  The dataset captures key issues when working with remote sensing such as multiple sensors, domain shift, and imbalanced label classes.  \n\nQuality: The annotations were generated by geography experts at IGN.  The baseline methods based on U-Net are reasonable.  Models are trained 5 times so that standard deviations can be captured.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "fiYY5zV1qg", "reviewer": "Reviewer_GDnf", "summary": "", "questions": "", "limitations": "The authors have reported the main limitation of the datasets. However, I shared some additional reflections: \n\n- The presented baseline seems to marginally improve the results using the time series information from Sentinel-2 data. This could be intrinsically related on how the label have been generated. The annotators have use the VHR images as reference to recognize  the different land covers. \n- The dataset is acquired only in France and I am not sure that we can use for investigating a domani-shit\n- The dimension of the S2 patches is an arbitrary choice (maybe driven by the model architecure used as baseline) ", "rating": 9, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Large and well organized dataset\n- Good quality very high resolution land cover labels \n- Challenging task to develop models able to learn from multi-resolution and multi-temporal data", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "E5kh9fIupM", "reviewer": "Reviewer_j7rF", "summary": "", "questions": "", "limitations": "The motivation for the dataset in the paper is not clear, and the design of the baseline model architecture lacks basis.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "FLAIR dataset utilizes multi-source data (very high resolution aerial images, elevation and Sentinel-2 time series) to solve heterogeneous spatial, temporal, and spectral resolutions domain shift in Country-scale land cover semantic segmentation, which has important application prospects in the field of multi-platform Earth observation.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: The paper under review presents a geospatial, high-resolution semantic segmentation dataset covering French urban scenes. Multiple reviewers have provided their feedback and suggestions, and the authors have responded to these comments and made revisions.
Reviewers have highlighted several strengths of the paper:

•	The dataset is well-organized and of high quality.

•	The paper addresses the challenge of developing models capable of learning from multi-resolution and multi-temporal data, making it significant.

•	The paper represents an important contribution to both the remote sensing and deep learning communities.

Several concerns and suggestions for improvement have been raised by reviewers, and the authors have addressed these effectively:

•	Detailed information about the annotation procedure and cloud cover filtering has been added to the manuscript.

•	The authors have provided explanations for the choice of Sentinel-2 imagery and discussed the limitations and advantages.

•	They have incorporated a comparative analysis using different models, addressed metadata encoding methods, and clarified terminology.

•	Visualizations of time series changes have been added to aid understanding.

•	The authors have explained the choice of patch size and addressed concerns about transferability outside of France.

•	Additional visualizations and clarification of motivation have been provided.

While some suggestions, such as using transformers instead of MLP in a specific module, were not fully adopted, the authors have provided reasoning for their choices. Discrepancies between tables have been clarified.

Overall, the authors have diligently addressed the comments and suggestions from the reviewers, enhancing the clarity and quality of the paper. The reviewers' concerns have been effectively resolved. Considering the positive feedback and the authors' thorough responses, the paper is well-prepared for acceptance.

PS: the criticism related to the page limit is clearly explained by the authors, thus, did not impact the decision. After removing the extremely low rank due to this point, this paper shall have an average score of 7.
Decision: Accept (Poster)