SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data
Authors: Mélisande Teng
Amna Elmustafa
Benjamin Akera
Yoshua Bengio
Hager Radi
Hugo Larochelle
David Rolnick
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: remote sensing
biodiversity monitoring
species distribution modelling
citizen science data

Abstract: Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary to ensure food, water, and human health and well-being. Understanding the distribution of species and their habitats is crucial for conservation policy planning. 
However, traditional methods in ecology for species distribution models (SDMs) generally focus either on narrow sets of species or narrow geographical areas and there remain significant knowledge gaps about the distribution of species. A major reason for this is the limited availability of data traditionally used, due to the prohibitive amount of effort and expertise required for traditional field monitoring. 
The wide availability of remote sensing data and the growing adoption of citizen science tools to collect species observations data at low cost offer an opportunity for improving biodiversity monitoring and enabling the modelling of complex ecosystems. We introduce a novel task for mapping bird species to their habitats by predicting species encounter rates from satellite images, and present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird, considering summer (breeding) and winter seasons. We also provide a dataset in Kenya representing low-data regimes. We additionally provide environmental data and species range maps for each location.  We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks. SatBird opens up possibilities for scalably modelling properties of ecosystems worldwide.

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data
Mélisande Teng
Amna Elmustafa Mila, Stanford University
Benjamin Akera
Mila, Université de Montréal tengmeli @mila. quebec
Mila, McGill University
amna. elmustaf aaaassafa@milla la. quebec quebbc
akeraben@mi la, quebec
Yoshua Bengio
Hager Radi Abdelwahed
Mila, Université de Montréal yoshua. bengio@mila quebec
Mila
hager radi mrad@mill.... iaila.quebeee
Hugo Larochelle
David Rolnick
Mila, Google Research, Google Deepmind hugolarrochelle@gooog// com
Mila, McGill University
drolnickécs dro.nick@cs.mogill..c
Abstract
Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary ensure food, water, and human health and well-being. Understanding the distribution of species and their habitats crucial for conservation policy planning. However, traditional methods in ecology for species distribution models (SDMs) generally focus either on narrow sets of species or narrow geographical and there remain significant knowledge gaps about the distribution of species. A major reason for this is the limited availability of data traditionally used, due the prohibitive amount of effort and expertise required for traditional field monitoring. The wide availability of remote sensing data and the growing adoption citizen science tools collect species observations data low cost offer an opportunity for improving biodiversity monitoring and enabling the modelling of complex ecosystems. We introduce novel task for mapping bird species their habitats by predicting species encounter rates from satellite images, and present SatBird" satellite dataset of locations in the USA with labels derived from -abennce-bbecce observation data from the citizen science database eBird, considering summer (breeding) and winter seasons. We also provide a dataset Kenya representing low-data regimes. We additionally provide environmental data and species range maps for each location. We benchmark set of baselines on our dataset, including SOTA models for remote sensing tasks. SatBird opens up possibilities for scalably modelling properties of ecosystems worldwide.
1 Introduction
Climate change is a major driver of biodiversity loss, affecting ecosystem services which, in turn, im- It is crucial to understand the changing distributions of species globally to inform policy decisions, for example in shaping land use and land conservation choices. However, there remain large knowledge gaps about species distribution, due principally to the amount of effort and expertise required for traditional field monitoring. Furthermore, mapping species habitats at scale can be challenging habitats have varying scales depending on species.
pacts various human wellbeing aspects 32 45
Project Website: tttss///aabird.gihubb.
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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For example, song sparrows are found in a wide variety of open habitats, including tidal marshes, agricultural fields, overgrown pastures. forest edges, and suburbs whereas Kirtland's warblers only breed in young jack pine forests. Traditional methods for species distribution models (SDMs) use environmental data to predict the distribution of species across geographic space, However, they generally focus either on narrow sets of species or narrow geographical areas, while species interact with each other, and multi-species modelling is needed for understanding ecosystems. The limitations of traditional methods lie in their computational cost, the insufficient ability of models to account for complex relationships between different variables, and the limited availability of the type of data used [50 at fine-graine fine-grained resolution.
Machine learning algorithms for remote sensing have increasingly seen wide applicability across sustainabilit--relttd domains (see e.g. (55)) and have been suggested as a promising tool for SDMs [] Moreover, the surge of data collection on citizen science platforms along with improved data quality validation processes in recent years offers tremendous opportunity for scientific research, as species observation records from these sources can cover a larger temporal and geographic extent at a finer resolution and at lower cost than traditional sampling methods. Indeed, citizen science data and remotely sensed ecosystem attributes such as vegetation indices have been shown to improve the performance of models, especially for less widespread species 4 Recently, the GeoLifeCLEF challenge [41 was introduced with the goal of directly predicting plant and animal abundance from im-resolution aerial images, using deep computer vision. However, the GeoLifeCLEF benchmark has proven extremely challenging, potentially since only one species (out of 17,000 overall) is associated with each location, even though numerous other species could likely be found in the same place.
We propose to use remote sensing to infer the joint distribution of many species for given location, using publicly available citizen science observation records as ground truth. Our approach leverages the fact that a species' presence or absence at location depends on the ecosystem present there, and therefore the abundances of different species are highly correlated. We present SatBird, dataset and benchmark for the task of jointly predicting the encounter rates of bird species. Encounter rates are a widely used measure in species distribution modeling, and correspond to the probability of an observer to encounter this species if they visit a given location.
Our contributions include:
Proposing and framing the task of predicting bird species encounter rates jointly at a specific location using remote sensing data (Section 2
We introduce SatBird, a dataset for the above task, obtained from publicly available bird observation and satellite data sources (Section D. composed of the following sub-datasets:
(i) USA summer dataset, generally corresponding to the breeding season, (ii) USA winter dataset, the nonbreeding season, (iii) Kenya dataset, as an example of a low-data regime. We benchmark a variety of popular models on SatBird and show the efficacy of deep computer vision methods for this task (Section 5)).
We release the dataset and code for dataset preparation and benchmark making possible to easily extend the dataset to other regions in the world.
2 Task definition
We propose to predict bird encounter rates from remote sensing data with the goal of completing species distribution mapping in places that have not been surveyed. We use bird sighting records from the citizen science database eBird (36), which has 80 million records of almost all 10000 global bird species. In particular, we leverage observation reports called complete checklists. Each location (termed a hotspot) in the cBird database is associated to a number of complete checklists containing all species birdwatcher was able to observe at specific date and time. Complete checklists are presence-abes abbencceee data -reflecting both the presence of reported species and the absence of non- reported species- and therefore may be considered almost as informative expert field surveys. If h is a hotspot, and $1...., Sn the species of interest, then our goal is to build a machine learning model that takes as input satellite image of h (and optionally other data) and predicts the vector y" = (0") ..., y ). where v" is the number of complete checklists reporting species s at h divided by the total number of complete checklists at h.
This ratio y can be understood as an encounter rate, the probability for a visitor to observe a species if they visit the same location (hotspot). We aim to jointly predict this quantity for all relevant bird
2

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Operniuu
Bentinel-2 RGB
eBird
Complete checklists
Sentinel-2 NiR
Target encounter rafes
Predictions
D 0. 0.5 0 0. 0
0.5 0.6
Loss
Bloclimatic rasters
Match
Correct birds
Model
Concatenate
Binary maaks
0
Pedologic rasters
SOLGANDS
.Rnne maps speciesr..
TheComelll.b
Powared by wttidd
Figure 1: An overview of data streams for SatBird, as well as inputs and outputs for the task for predicting species encounter rates. Sentinel-2 10m-resolution satellite data can be used along low resolution environmental data as input to a model after matching their resolutions. Labels are derived from eBird complete checklists. Observations of vagrants (migrating birds) the labels are corrected with range maps from eBird, which can also be incorporated in the model to make it geography-award
species. Thus, our task can be considered as a supervised multi- regression problem. We chose to predict encounter rates as they are an important quantity in ecology, and they are also used extensively in the form of "hotspot bar charts" in the eBird platform in order to summarize the species given location for birdwatchers and ornithologists. These bar charts are used to suggest to users what species may be expected in particular location. However, they rely only past data, and the suggestions cannot be extended to places that have not yet been visited. Moreover, encounter rates from eBird have not been modeled jointly to take the interaction between species in the same habitat into consideration, which is important to inform species presence estimates. Our goal is to bridge this gap by modeling the encounter rates of many species jointly and predict them in locations with few or no observations. An overview of the interaction between our data and models shown in Figure
Related Work
There have been increasing number of citizen science initiatives collect species observation data for a variety of taxa in the past decade, from butterflies, to plants, to sharks [26] (58 [1]. Indeed, by crowdsoureing data collection efforts, it is possible to gather data not only over a larger temporal and geographic extent but also at fine resolution [16]. As a result, the number of papers using citizen science for SDMs has increased at approximately double the rate of the overall number of SDM papers [22). eBird data has been increasingly used for scientific research on birds, from assessing climate change-driven vulnerability of species, modelling population change to predicting virus transmission [59 35| 12). The eBird status and trends project [24] from the eBird team combines satellite images with raw eBird data and uses statistical models and machine learning to build visualizations and tools to better understand migration, abundance patterns, range boundaries, and
other patterns around the world. Other large scale citizen science databases used for SDM include iNaturalist 31 in which users record observations of species by taking geolocated pictures. In total, has 1 13 million species observations. However, because limited to observations with pictures, it is more prone to sampling biases, both geographically and towards certain species (c.g. larger,
3

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more brightly coloured species [0). It is also presence-only that is, while there is data on the presence of certain species at given location, assumed that other species may also be present.
Remote sensing data has been used for a variety of biodiversity monitoring applications, including predicting land cover classes for downstream ecological modeling [3, measuring the size of groups of animals [63], identifying tree species from crowns [61], and localizing bird nesting sites [29]. Bioacoustics has been used for bird monitoring [2] but one of the main limitations relates to the detectability of species, for example in densely populated areas with many anthropogenic sound sources 255 Therefore, we will focus on discussing related work with remotely sensed imagery. In the GeoLifeCLEF 2020 challenge [14], they proposed task combining remote sensing and citizen science data, where the goal is to predict the localization of plant and animal species using 1.6M geo-localized observations from France and the USA of 17.000 plant and animal species from aerial images and environmental features. The labels are derived from citizen science databases of presence-only species observations and each location is associated with only one species, limiting the ability to accurately model multiple species at a time. A newer version (2023 edition) of this classification challenge proposes to focus only on plant species and to use Sentinel-2 satellite images rather than aerial images. While each location is still associated with one species, a validation set with presence-absence labels is provided, highlighting the importance of presence-absence data, at least for the evaluation of such species modeling tasks. Methods deriving information from satellite images have also been developed in the context of avian studies but they usually use suite of carefully selected measures calculated from the imagery rather than leveraging its full potential [5 21.
A particular field of application that saw the development of large remote sensing images datasets is
agriculture, with an emphasis on the temporal resolution for crop monitoring [28 44 62] Recently, a
number of methods have been introduced to learn robust representations of remote sensing data, with the goal of using them for a variety of downstream tasks [44 52, 53 In particular, MOSAIKS (55) proposes to use single encoding of satellite imagery for diverse prediction tasks, and Satlas 7 and SatMAE 15I provide pre-trained models on a large number of remote sensing images, arguing that they can be useful feature extractors for remote sensing tasks.
Compared existing datasets and methods for SDM, our proposed approach has several advantages:
We fully leverage presence-absence data from a large citizen science database and compute encounter rates such that biases due to the different number of observers visiting locations are mitigated (see Section 2)
We enable modelling of a wide range of species a time across large geographical areas. To our knowledge, this is the first attempt at predicting encounter rates for many species jointly, building on recent advances in machine learning for remote sensing.
Our proposed pipeline makes it easily extendable to other regions in the world as data sources are open; eBird [36] data is available in all countries in the world and satellite Sentinel-2 data is publicly available.
4 SatBird Dataset
We introduce SatBird, a dataset for the task of predicting encounter rates defined in Section 2 from remote sensing images and environmental data with labels derived from eBird observation reports, with the continental USA and Kenya as regions of interest. Observations in eBird are particularly skewed towards North America, and we choose the USA as a first region of interest, given the extensive and reliable data available on bird observations. We consider the summer season (June-July) and the winter season (December- JanoarrrrJJrrr) separately. For most North American bird species, the summer reflects breeding season range, while the winter reflects nonbreeding range. Note that we omit the spring and fall migration seasons, in which species distributions may vary dramatically, and which are also often of less interest to ecologists than breeding habitat.In Kenya, we consider the distribution of birds all all-year-round as migration is negligible.
In this section, we detail the data sources and data preparation for SatBird for the USA summer and winter seasons and Kenya. Table [ summarizes the data for each of the considered subsets SatBird- USA -smmmer, SalBird-USA-winte and SutBird- SutBird-Kenya Figure summarizes the different sources of data for SatBird.

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USA-summer 122593
USA-winter
Kenya
Number of hotspots
53361 670
9975
Number of species
670
1,054
Satellite RGBNIR reflectance Satellite true color image Bioclimatic
Pedologic rasters
Range maps
(586 species)
(620 species)
Table Summary the data provided for cach the SatBird subsets: USA -ummerr, USA-winter, and Kenya.
4.1 Bird species data
We used complete checklists from eBird [36]. For USA, we extracted checklists recorded from 2010 to 2023 at hotspots in the continental USA in June-July and December-January for the summer and winter seasons respectively. We filtered out hotspots with fewer than complete checklists recorded, to ensure more meaningful encounter rates. We also excluded locations in the sea using the 5-meter resolution US nation cartographic boundaries provided by the Census Bureau's MAFTTIGER [48). We included all regularly occurring species in the region, denoted by ABA Codes 1 and 2 [I], which include regular breeding species and visitors. Code-1 species are more widespread, while Code-2 species have restricted range occur in lower density, bur still widespread. We omit species found only in Hawaii and Alaska, as well as one Code-2 seabird species with rare oceanic observations, leaving total of 670 species.
For Kenya, we extracted complete checklists recorded from 2010 to 2023, limiting ourselves the 1,054 species found regularly in Kenya according Avibase [6. Due to the scarcity of data in this region, we applied no criteria regarding the minimum number checklists per hotspot or the month in which an observation was made. There are also a larger number of non-migratory birds present in Kenya than the USA, further motivating our choice to consider records aggregated across seasons.
An additional preparation step was merging hotspots with different IDs in eBird that had the same latitude and longitude, pooling the checklists together, unless all checklists were reported by the same observer on the same day, in which case we dropped the hotspot. We then computed the encounter rates for each hotspot as explained in Section @ and corrected for vagrants (species seen hotspots outside of their geographical range) by using range maps from eBird to set the target encounter rates to zero in these hotspots when they were available. We aggregated the species observations over 13 years, only considering seasonal change in distributions in the USA and leaving annual temporal change for future iterations of this work. As we aggregated data per hotspot, we did not take into account the reporting patterns of birders. Previous work (34) demonstrates that estimating the observer expertise can improve species distributions from eBird data. This may also be considered for future iterations of this work.
4.2 Environmental data
Following the Geol LffeCLFF 2020 dataset [14] we extracted 19 bioclimatic variables rasters of size 50 x 50 and 6 x for the USA and Kenya datasets respectively and of spatial resolution approximately 1 km centered on each hotspot from WorldClim 1.4. Bioclimatic variables often used to model species distributions as a function of climate [33]. They represent annual trends for temperature, precipitation, solar radiation, wind speed and water vapor pressure. Additionally, for the USA dataset, we also extracted pedologic (soil) variables with resolution 250 meter from SoilGrids [30], which provides global soil properties maps, including pH, soil organic carbon content and stocks. These maps are obtained with machine learning methods trained on soil profile observations and environmental covariates derived from remote sensing. We provide more details the environmental variables in Appendix A
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(a) SiBBird-USA-summe
(b) SatBird-USA-wime USA-winter
SatBird-Konn -ennya
Figure 2: Distribution of hotspots across the training, validation, and test sets.
4.3
Satellite data
For each hotspot, we extracted data at 10 meter resolution for RGB and NIR reflectance data from Sentinel-2 satellite tiles covering square of about 5 km2 centered around the hotspots. Additionally we also extracted the true color image RGB bands. We extracted images with cloud coverage of at most 10%, keeping the least cloudy image in the time windows (June 1 - July 31, 2022) for USA- summer, (December 1, 2022 January 31, 2023) for USA-winter, and (January 1, 2022 January, 1 2023) for Kenya datasets respectively. In the case where image was found for hotspot with these criteria, we composed mosaics with images with up to 25% cloud coverage the considered time window and taking the median values. We associate a recent satellite image is best representative of our species data, since there are more checklists in recent years compared earlier years.
image per hotspot, considering that
4.4 eBird range maps
We extracted range maps from the eBird Status and Trends [24] for the summer season in the USA from eBird by filtering ranges between June and July 2021 which are available for 586 of the 670 species considered in the USA. For the winter dataset, we were able to extract range maps for 620 out of 670 species using the same process but filtering ranges between December and January using the package ebirdst [17]. For each hotspot I, we construct binary mask my of size 670 where
(4) if is within the range of species i. Range maps were not available for Kenya data.
my
4.5 Aligning data
Finally, we aligned the satellite and the species observation data obtain the final set of hotspots included in the SatBird dataset, as some satellite images were not found (for example if the cloud cover criterion was not met). Only hotspots with corresponding satellite image with height and width greater than 128 pixels were kept, with their associated the environmental data rasters. In this way we obtained 122,593 hotspots for the USA summer season, 53,361 for the USA winter season and 9,975 in Kenya. In summary, each hotspot is associated with the following input data: I satellite image with RGB & NIR reflectance (4) + RGB true color bands (3), bioclimatic rasters (19), and pedologic rasters (8). The targets are computed from the aggregated checklists, and correspond to species encounter rates. Figure 3 shows the distribution of the number of species per hotspot.
4.6 Dataset splits
In order to account for spatial cotte-oorelltion and overfitting that can arise from random splits of geospatial data [54], we use the scikit-leard DBSCAN algorithm [99 implementation to cluster hotspots DBSCAN finds core samples and expands clusters from them, and we specify core samples to have at least hotspots with a maximum allowed distance between them of 5km. We obtained 12,650 clusters which were then randomly assigned to train, validation and test splits such that they respectively contained 70%, 15%, and 15% of the hotspots for the US summer dataset. We kept the same split assigment for winter US locations also present in the summer US locations, assigned the

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rest in a 70 %. 15 %, 15% repartition in the train, validation and test sets. The same procedure was followed for the data in Kenya. Figure2 shows the spatial distribution of hotspots for the dataset and we provide number of hotspots per split in Appendix A
Benchmarking
In this section, we present the baselines used to benchmark our task, predicting species encounter rates. We choose a wide range baselines covering simple regression trees, deep CNNs and transformers.
5.1 Models
We propose first basic baseline which is simply the average of encounter rates over the training set for each species.
Mean encounter rates:
Environmental baseline: Following the GeoLifeCLLFF challenge, we propose an environmen- tal baseline, using Gradient Boosted Regression Trees on the bioclimatic and pedological variables extracted at each of the hotspots.
This model was proposed as an accessible method that generalizes well across wide range of tasks at significantly lower computational cost compared to training a deep neural network for each task. It performs a one-time unsupervised image featurization by convolving set of randomly selected patches with the input satellite image. These features can then be reused for an unlimited number of tasks. We propose to use the MÓSAIKS method to extract features from our satellite images dataset and combine them with the environmental variables before training regressor to predict the encounter rates at each hotspot.
MOSAIKS [55:
SATLAS pretrained transformer
The literature suggests pretraining Swin-v2 former on a large number of remote sensing images to target distribution shifts in remote sensing tasks. The model pre-trained on Satlas, dataset of 1.3 million satellite images from different sources, and is proven to perform well in both in-distribution and out-of-distributie settings. They provide weights for models mreitrained on low-resolution and high resolution images. We freeze the Satlas parameters and train final fully connected layer to predict our target.
40 trans-
This is an unsupervised pre-training framework based on Masked Autoencoders (MAE) for tasks related to remote sensing, specifically for temporal or multi-spectral satellite imagery data. It has been shown to perform well on supervised and transfer learning tasks. We apply transfer learning our dataset using SatMAE pre-trained model on the IMoW 13 dataset. We use the pre-trained model as a feature extractor and train dense layer to predict the species encounter rates.
SatMAE [5]:
R8 [27::
CNNs have been widely used as feature extractors for remote sensing imagery. We R8 27 architecture, using the RGB and NIR bands reflectance data input and initialize the network with ImageNet pretrained weights. Since ImageNet has only RGB bands, the initialization of the first layer for the NIR band is performed by sampling from normal distribution parameterized by the mean and standard deviation of the layer's weights for the other bands. We also experiment with RGB true color images as input and compare the results against reflectance data.
5.2 Including geographical information
We propose to make the CNN baseline model spatially explicit to account for the geographical ranges of some species. For example, Eastern and Western wood pewees have the same habitat but are found in different regions and do not co-occur. A model blind to geographical information might predict both species in the same place, which is undesirable when producing maps for conservation planning. and could also impede training. We propose to use range maps from eBird, constructing binary masks to zero-out the predicted encounter rates species when a given location falls outside of their geographical range. For location 1, the range mask is vector of size equal to the number of bird species, and rf if outside of species geographical range, and r = 1 otherwise. If the range map is not available for species, the predictions are left untouched (rf for all species). For species if of is the logit, the final prediction is then 8( (2))) (6)

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5.3 Evaluation metrics
Beyond optimization for common regression metrics (MSE, MAE), it desirable for the predicted most likely species in given hotspot to coincide with those that have been observed most frequently. In fact, while we model many species jointly, our targets are zero-inflated due to the absence of species many hotspots, which is frequent challenge in SDM 46 38 We therefore report top- 10 and top-30 accuracies, representing for = 10 and & = 30 the number of species present in both the top & predicted species and the top k observed species, divided by k. However, the number of species observed varies considerably across hotspots (Figure 33. unlike in the GeoLifeCLEF challenge for which there is only one ground truth species for each hotspot. We therefore also define an adaptive top-k accuracy as the top-k value where le is the total number of species observed at that hotspot.
6 Experiments
6.1 Experimental details
In our experiments, we consider a region of interest of 640 m², center-croppin the satellite patches to size 64 x 64 around the hotspot and normalizing the bands with training set statistics. When adding environmental data, we match their resolution to that of the satellite images. Random vertical and horizontal flipping, random noise, blur, contrast and brightness were used for data augmentation. Models are trained with cross entropy loss: Lc No Σn Lu = NA En Σ stspecies) -y (og) (1-9)) log(1 - -f)), where N₁, is the number of hotspots h, y the predictions and the ground truth encounter rates.
Satlas and SatMAE models use the true color images as input. They do not support the use of environmental data. The input is further processed by normalizing its values between 0 and 1. For MOSAIKS, we extracted 1024 features for each true color image, combined them with the environmental data and trained an XGBoost regressor on them. For the Res.Net-18-basee models, we experiment with different inputs: RGB true color images, RGBNIR reflectance values, and RGBNIR reflectance values combined with the bioclimatic and pedologic data. We normalize the bioclimatic and pedologic data variable-win wise with training set statistics, aligning them to the satellite images' resolution and stacking the corresponding patches to the images.
All models were trained with batch size 128 using Adam (37 optimizer. Each experiment is run on seeds and results report the average of 3 seeds on the test Training, validation and test sets follow the splitting described in Section 4 All ResNet-18 18 models were trained for 50 epochs. Satlas and SatMAE were trained for 100 epochs, freezing the pretrained model and training only the last layer. More details about experiments including resources used are available in Appendix B
6.2 Results& & discussion
In this section, we present results for all our baseline models using SatBird-US.. USA-summer and SatBird- USA-winter shown in Tables 3 respectively. Validation set results for both datasets are reported in Appendix C We also report baselimes for HetBird-Kenye in Appendix D
We observe that models trained with satellite images only, either the true color image or the re- flectance values, do not outperform the environmental baseline. However, combining satellite images with environmental rasters results in a significant improvement, in all metrics, with the highest improvement in the top-30 (13%), top-k (4%), and top-10 (8%) metrics in SatBird-USA Soiiiid-USA-uummme Assmmer and improvement in the top-30 (10%). top-k (4%), and top- 10 (5%) metric for SatBird-USA -S---itterrr compared to the environmental bascline. This highlights the value of satellite data for our task, and also the importance of using both environmental and remote sensing data. The models using environmental data along with either RGBNIR reflectance images or RGB visual images achieve nearly-eque -eaall competitive results, but our best models use RGB images, along with environmental data, titled ResNet- 18 (img+env) in Tables 2 and 3 We suggest incorporating other information as input such land cover and altitude data which can be obtained from publicly available sources [18 for further improvements.
For the rransformer-baser models, both Satlas and SatMAE do not outperform ResNet-18 18 when applied to the true color image only. This observation suggests that we may need further finetuning for these models on our data rather than freezing the model weights. We note that Satlas outperforms
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Model
MAE[1e-2]
MSE[1e-2]
Top-10
Top-30
Top-k
Mean encounter rates
3.1 0.0
0.0
0.0
38.6 0.0
44.8 - 0.0
Environmental baseline
2.3
0.6
0.1
57.8 + 0.1
63.8 + 0.1
Satlas (img)
2.01
0.0
0.1
46.1 + 0.0
48.6
SatMAE (img)
3.0
0.9
2.1
66.5
MOSAIKS (img+tev)
2.0
0.7
4.0
56.7 4 0.0
62.2 =: 0.0
ResNet-18 (img)
0.02
0.8
0.1
52.2 + 0.2
54.5 + 0.2
ResNet-18 18 (reff)
2.6
0.8
3.11
53.3
55.5 + 0.1
ResNet-18 (img+env)
2.2 0.02
4.. 0.0
66.9 0.1
ResNet-18 (refl+env)
2.1 #.01
0.0
4.1
65.1 0.2
66.6 = 0.1
R8 (refl+env+RM)
2.16 + 0.0
0.65 #.0
4.2
65.1 + 0.1
66.7 + 0.1
Table 2: Test results on the SatBird-USA-Summer
Best results are shown in bold. img refers to using the RGB true color image, reft refers to using RGBNIR reflectance bands, env refers to using the environmental data (bioclimatic and pedologic variables). RM refers the use of range maps.
Model
MAE[]e-2]
MSE[]c-2]
Top-10
Top-k 51.4 0.0 68.5 6.0
Top-30
Mean encounter rates
2.5 + 0.0
0.7
2.0
4.0
Environmental baseline
1.8
0.4
4.0
Satlas (img)
0.0
0.7
3.16
5.15
54.1
SatMAE (img)
2.4
0.7
2..6
0.1
5.16
MOSAIKS (img+env)
1.9
0.5
4.0
6.01
6.04
ResNet-18 (img)
0.2
0.6
0.44
0.2
5.4
R8 (reff)
2.0
0.6
0.9
5.9 5.9
ResNet-18 18 (img+env)
1.69
0.4
0.3
69.5
70.9
ResNet-18 (refltenv)
1.67 0.0
0.4
5.1
70.8 0.1
ResNet- 18 (refl+env+RM)
1.69 0.0
0.4
51.0
69.4
71.0 + 0.1
Table 3: Test results on the SatBird-USA-winter
Best results are shown in bold. img refers to using the RGB true color image, reft refers to using RGBNIR reflectance bands, env refers using the environmental data (bioclimatic and pedologic variables). RM refers to the use of range maps.
SatMAE, likely because the former was pre-trained on a very large dataset of Sentinel-2 images images. For SatMAE, the model
which is the same source (and has the same resolution) was pretrained on the fMoW dataset with higher resolution (0.5m) than the images in our dataset. MOSAIKS also performs reasonably well compared to other deep models (ResNet-18 for example) for the true color image and environmental raster input, considering that lightweight model.
computationally
We also observe in Tables 2 and that using RGBNIR reflectance bands as input yields better results than using true color images (while not using environmental data), given the information added by the NIR channel. This aligns with previous conclusions [4] that remote sensing indices using the NIR band, such as the NDVI improve the performance of SDMs. Moreover, adding range maps, does not hurt the model but it does not seem to add a significant contribution. Range maps can still be useful In partieular,
in cases where the data is not abundant to use this approach whenever available with smaller-sized datasets. We had experiments with using latitude and longitude inputs location encoder, concatenating their features with the model features but we found lower performance for this baseline (see Appendix B.5
still recommend
Conclusion
In this work, we present and release SatBird, a large-scale dataset for species distribution modeling using remote sensing and citizen science data. SatBird includes datasets the USA for summer and winter seasons and one dataset in Kenya. We detail the process for building SatBird, as well as using with different models. We also present benchmark results for the dataset on a wide range of popular remote sensing algorithms. Two limitations of the present work include using a single satellite image to represent season, while the landscape can change over time, and that satellite images are extracted from one recent year, while eBird data is aggregated across multiple recent years.

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We are planning next version of SatBird that will include multiple satellite images for each hotspot account for changes over time. We also aim expand SatBird using citizen science data for other organisms, not merely birds (this is more challenging since most such datasets are presence-only).
As SatBird is intended to directly impact biodiversity monitoring, we look forward to integrating the approaches we have introduced into existing tools for ecology and policy, One immediate application for models trained on SatBird is eBird's existing tool that lists the "likely species" in given area, to be available for poorly monitored locations. This tool currently relies on encounter rates from past checklists and is therefore only available in well-monitored locations. Models trained on SatBird could estimate such encounter rates for poorly monitored locations via remote sensing. We hope such input will be valuable researchers seeking to understand biodiversity and climate change, as well as policymakers interested in evaluating conservation priorities across different areas of land.
Acknowledgments and Disclosure of Funding
This research enabled and funded by Mila (mila.quebec).. This project was supported by the Canada CIFAR AI Chairs program and Google-Mila grant. The authors grateful to Sal Elkafrawy for their contribution in adapting the MOSAIKS baseline code and the Mila IDT team for technical support.
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A Further Dataset Description
A.1 Number of species per hotspot
A well-known issue in species distribution modeling when looking at many species at time is the zero-inflated nature of the targets, which also appears in our dataset. Indeed, all 670 considered species in the USA and 1054 species in Kenya are never found in the same place together. Figure 3 shows the distribution of the number of species with non-zero encounter rates per hotspot in the Ce-BirddUS-sumann and the HuuBird-Kenyee datasets.
2500
2020
Number of holspots
Number of hotspots
2002
2000
1000
202
300
200
Number of species
Number of species
Figure 3: Distribution of the number of species encountered per hotspot for the USA-summer (left) and Kenya (right) training sets. On average a hotspot in these datasets has resp. 48 and 30 different species reported.
A.2 Dataset splits
Table desccribs the number of hotspots present in each split for each dataset of SatBird. The splits were obtained by following the process described in Section
USA summer
USA winter
Kenya
train
85553 18511 18529
38241
6988
validation
8323
1658
test
6797
1329
Table 4: Number of samples in each split for the three datasets
A.3 Environmental variables
In Table[5] we provide a description of the environmental variables provided in the SatBird dataset. The bioclimatic data taken from the highest resolution data available (30 seconds resolution, corresponding to about 1km2) from WorldClim (20) 23 Data for the USA dataset was obtained following the method proposed by [14, and we used WorldClim1. 1.4 which has data aggregated from 1990. For Kenya, extracted data from 2orldClim2.1 which has data aggregated for 1970-2000. The soil data comes from SoilGrids and has resolution 250m [30].
A.4 Distribution of the targets
Figure 4 showss the distribution of species by average encounter rate over the USA-summer training set hotspots. Most species have a low average encounter rate reflects both the zero-inflated nature of the targets. In Figures 5 and we show the distribution of encounter rates for very common species all across the USA, the A American robin (Turdus migratorius) and the California quail (Callipepla californica) whose range only extends to the west of the USA. If we compare the two distributions.
14

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Bioclimatic variables (30 aresec resolution)
Name
Description
bio 1
Annual Mean Temperature
bio 2
Mean Diuraal Range (Mean of monthly (max temp min temp))
bio 3
Isothermality (bio 2abo_2/boo_7] (* 100)
bio 4
Temperature Seasonality (standard deviation * *100)
bio_5
Max Temperature Warmest Month
bio 6
Min Temperature of Coldest Month
bio_7
Temperature Annual Range (bio_5-bio_6)
bio 8
Mean Temperature of Wettest Quarter
bio_9
Mean Temperature of Driest Quarter
bio 10
Mean Temperature of Warmest Quarter
bio 11
Mean Temperature of Coldest Quarter
bio 12
Annual Precipitation
bio 13
Precipitation of Wettest Month
bio 14
Precipitation of Driest Month
bio 15
Precipitation Seasonality (Coefficient of Variation)
bio 16
Precipitation of Wettest Quarter
bio 17
Precipitation of Driest Quarter
bio 18
Precipitation of Warmest Quarter
bio_19
Precipitation of Coldest Quarter
Pedologie variables (250m resolution)
Name
Description
oredre
Soil organic carbon content (g/kg at 15cm depth)
phihox
Ph 10 in H20 (at 15cm depth)
cecsol
Cation exchange capacity of soil (cmolc/kg 15cm depth)
bdticm
Absolute depth to bedrock cm
clyppt
Clay (0-2 micro meter) mass fraction at 15cm
sltppt
Silt mass fraction at 15cm depth
sndppt
Sand mass fraction at 15cm depth
bidfic
Bulk density in kg/m3 at 15em depth
Table 5: Description the bioclimatic and pedologic variables extracted from WorldCliml.4 and SoilGrids.
we see that for the California quail, more hotspots have zero encounter rate which is consistent with the fact that its range is smaller. shows in which states the species is found, namely California and neighboring states.
Histogram of spoctas by encounter rate
Histogram of spocies by encountar rate 0.1
350
230
NUmber of species
miher scee lovectes
200
250
202
150
150
300
50
0
Figure 4: Distribution of species by average encounter rate over the summer training set (left).
The figure on the right zooms in the histogram for species with encounter rate < 0.1
15

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0.0
52
0.4
56
0.5
1.0
Encounter rate
Figure 5: Encounter rates distribution of the American robin across the USA Uummer training set
250
10200
00202
200
400000
Number of
100
50000
50-
10000
rate
Figure 6: Encounter rates distribution of the California quail across the USA-summer training set. We whos the full distribution (left) and the distribution for hotspots with non-zen conzeero encounter rates(right).
A.5
Dataset format
For each SatBird dataset subset, we provide a tta.ggg .z file which is organized as follows:
Dataset name (USA-sunmer, USA-winter, Kenya)
images/
images_ images_visuall visual/
parrected_targett
envirommental_datel
train, valid, test cav files
apecies_list.c. (file with the scientific name of the considered species)
Each hotspot has corresponding file in each subfolder. images contains the RGBNIR reflectance data in the format of tif file, images_vii amages_visual visual has the RGB true color images in the format of .tif file, environmemntal, _dattaaaaa has the environmental rasters in the format of .ny file, and corrected_ targets have the encounter rates for each hotspot in the format of joonn file. We provide more details about the files in the Datasheet for SatBird.
A.6 Where to find the dataset
We have released project website, where you can find links to access dataset and code. Our dataset is available for download at this link: /nttps://drive. www.le..ooo....oooooooooo.o..... www./drive/foolerss.
The companion code for the dataset
16

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State
Number of hotspots
California
3248
Washington
962
Oregon
921
Idaho
384
Nevada
168
Utah
65
Montana
10
Table 6: Distribution of hotspots in the USA-summer training set per state, for which the encounter rate (target) for the California quail is non-zero.
hww.:haw..oooooooiiiccccc preparation pipeline and benchmark can be found here: http:///gghhuuu.ccoooooockkkkaa SatBird
B Experiments
We have presented simple baselines to lay the foundations for the task regressing encounter rates, trying to cover different data inputs and different categories of models (Gradient-booster regression, CNNs, transformers).
We used internal cluster in our organization that has enough GPUs. For the deep vision ResNet baselines, on Nvidia A100 GPU, one epoch takes 54 minutes maximum (for the model Resnet- +((eef) with batch size of 128 and using 16 GB of RAM memory. Other models of Resnet-18 18 took less time. There are experiments where we used more powerful GPUs, resulting in less than day for training 50 epochs. Each baseline was trained times on 3 different seeds. As for the Satlas and SatMAE baseline, each experiment took two days to train on one GPU, We used Pytorch framework [49 in the implementation of our models.
For the MOS baseline, we extracted I. 1,024 features for each 10m-resolution image from Sentinel- 2 and tried both XGBoost and Ridge regression on the combination of the environmental and image features obtained with the Random Kitchen sinks methods proposed in MOSAIKS. The MOSAIKS model
(55] was originally trained on around Ixlkm 256x256 pixels RGB images (resolution about 4m/pixel) and 8.192 features were extracted for each of the training 100,000 images. In our case, we found XGBoost to perform better on the SatBird- Saaer--US-Suumee USA-Summer dataset and Ridge regression to perform better on the SatBird- --er- Winter and SatBird- Kenya datasets. Each model was trained with 3 different seeds and results each baseline are reported on the mean of the experiments.
Below, we add additional experiments on SatBird- Siiiid-USA-summme including: 1) hyperparameter search, 2) using pretrained weights from another remote sensing dataset, 3) using different loss functions, 4) training seperate models per bird species.
B.1 Hyperparameter search: learning rate
We conducted hyperparameter search a posteriori for the learning rate on the ResNet-18 (refl env + RM) baseline for the BatBrrd-USA-summeee
dataset for values in the range of (1 x 10-1, 5 x 10-3 ) and found that a learning rate of x 10-4 gave slightly better results. Hence, we used 10-4 4 in our reported experiments. We show the loss curves for a few models in our hyperparameter search in Figure 7
B.2 Initializing with pre-trained weights on a remote sensing dataset
We compare initializing the ResNet-18 (refl env RM) model with ImageNet weights (as reported in the paper) with an initialization from Seasonal Contrast weights 44]. We find that the models perform similarly reported in Table
17

---

1-00001
1+00000
0.09
0.08
1-00005
PH0000
H-000
6.088
0058
(a) Training loss vs epochs
(b) Validation loss vs epochs
Figure 7: Loss curves for ResNet-18 (ref++en++RM) models trained on SatBird-USA-summnn with different learning rates.
Model ImageNet initialization
MAB[1c-2]
MSB[1c-2]
Top-10 10
Top-30
Top-k
2.16
0.65
45.7
65.1
66.8
Seasonal Contrast initialization
2.2
0.65
45.6
65.1
66.6
Table 7: Test results the SatBird- -smmmer dataset for ResNet-18 (refl+env+RM) models initialized with ImageNet and Seasonal Contrast weights
B.3 Comparing different loss functions
Throughout the main paper, our experiments used cross entropy as the loss function as mentioned in Sec [ We have conducted further experiments using other regression loss functions such as L1 loss and L2 loss. We have also extended our cross entropy loss focal loss 39 to address the class imbalance. Table g shows that cross entropy loss achieves the best accuracies, and even the best MSE over the test set.
B.4 Could the model be better at predicting only one bird instead of the whole distribution?
Our goal with the Satbird dataset and task to predict many birds at once, and we expect that having information about other birds allows single model to learn from features that are common across many bird species and that it may indeed benefit predictions for the less common birds. We hypothesize that training single model to predict all species is better when modeling species distribution to capture interactions and co-occurrences among species. To verify this, we conduct experiments where we train ResNet-18 18 model (ref++env+RM) on 20 random species (including the most common and least common species), model on each these species (total 20 models) and
Model
MAE [le-2]
MSE [le-2]
Top-10
Top-30
Top-k
Cross Entropy
2.16
0.65
45.74
65.13
66.78
LI Loss
1.86
0.77
43.58
60.86
61.13
1.2 Loss
2.64
0.66
44.92
64.03
65.49
Focal Loss
2.93
0.75
39.43
57.99
60.68
Table 8: Using different loss functions for Resnet-18 baseline: this table compares the performance of Resnet-18 18 baseline when using different loss functions. Cross entropy Loss outperforms all other losses.
18

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x
Resnet18 670 species
0.20
Resnet18 species
Average MAE on test-set
Resnet18 species
0.15
0.10
0.05
0.00
Turdus m aigratorius
Ardea herodias
Coccyzus americanus
Gavia stellata
Setophaga caerulescens celss
Platalea ajaja
Eudocimus albus
Buteo albonotatus
Selurus aurocapille
Nyctanassa violacea
Aphelocoma cerulescees Callipelaa squamata Cygnus olor
Larus delaware warensis Setophaga tatpphaga&oo townsendi Bubo scandiacus Calidris alpina
Leucolia violiceps
Perisoreus canadensis Peucaea botteril
Figure 8: Training separate models per one species vs. training all species: Resnet18 670 species
refers to a model trained over all bird species. Resnet18 20 species refers model trained over 20 bird species only. Resnet18 I species refers to a model trained over each single species only as specified on the x-axis.
one model on all the 670 species. Then, compare the predictions of these models over the 20 species to the ground truth and report the mean absolute eтror (MAE). The difference in performance is, as predicted, quite small; with models trained on more classes (either 20 species or all 670 species) performing slightly better. Figure 8 shows that models trained on 20 or all species can be better than training model per single species. Even if may be better to train on less number of species, we suggest that the gain is much less than the compute overhead we will have when training many separate models per species.
B.5 Incorporating location information
Besides the range map masking method to make the CNN models spatially explicit, we also propose train location encoder taking as input latitude and longitude information jointly with the image encoder. We used the same architecture as the one proposed in [42 for the location encoder's architecture, removing the final classification layer, and instead concatenating the obtained features to those obtained from the ResNet image encoder, before passing them to a fully-connected layer obtain the predicted encounter rates. More specifically, we first map each spatial coordinate x² (latitude and longitude) to (oo((T=") (is sin(T=z'), ]. We end up with a input vector of size 4, which is passed through an initial fully connected layer, followed by residual blocks, each consisting of two fully connected layers with dropout layer in between. The performance of initial experiments with this setting on subset of the SatBird- -uriidd-UA--sunn USA-summer dataset was not better than what we obtained with range maps masking, and thus we did not explore further this method for this benchmark. However, could be that the location encoder's architecture or the fusion with satellite image features could be improved. We show results obtained on a small subset of an early version of the SatBird- US A-smmer dataset 9k hotspots) which led us to prefer exploring the range maps masking method in Table g
19

---

Model
MAE [1e-2]
MSE [1e-2]
Top-10
Top-30
Top-
Mean encounter rate
2.9 0.0
0.0
26.45 + 0.0
43.91 0.0
51.46 0.0
Env baseline
2.05 0.00
0.5+0.0
43.1 0.1
62.3
68.86 + 0.0
ResNet- 18 (reff)
1.98 +.88
0.5.:.0
41.8 +0.3
60.1 +0.2
67.0 +0.0
ResNet- (refl+env)
1.0
0.5++0.0
+0.0
67.0 +0.00
72.9 +0.2
ResNet- 18 (refl+env+LE))
1.0
0.1
44.5 +0.7
65.0 +0.0
72.0
ResNet-18 (refl+en++RM)
1.8+ 0.0
0.5 0.0
48.2 0.2
67.7 0.1
73.4 + 0.1
Table 9: Results for baseline models on the test set of a subset of SatBird-US Sottrrd-US-uummin env indicates the use of environmental variables. RM and LE refer the range maps and the location encoder respectively.
C Further Analysis on Sabird-USA- results
C.1 Validation set results
We report the performance of baselines on the validation set of the Table 10 and SatBird-USA -Sttiiiteeeeeer in Table 11 consistent with the reported test results.
dataset in
The results on the validation set for both datasets
Model
MA
1eE[1e-2]
Top-10
Top-30
Top-k 44.86 + 0.0
Mean encounter rates
3.1 + 0.0
0.9 1 0.0
0.0
$8.6
Environmental baseline
2.3 + 0.0
0.6::.00
43.1 0.0
57.7
63.9 + 0.0
Satlas (img)
2.8 0.0
0.0
0.1
46.3
48.9
SatMAE (img)
3.0
0.9
2.14
43.8
MOSAIKS (img+tenv
2.0
0.7
4.01
56.6
62.2 0.0
ResNet-18 (img)
2.0
0.00
3.29
0.1
54.8
ResNet-18 (reff)
2.0
0.7
3.02
53.8 1
56.01 = 0.03
R8 (img+env)
2.1
0.6
46.8
66.2 0.15
67.6 - 0.14
R (refl+env)
2.1 0.01
0.0
46.4 0.2
0.1
67.3 - 0.1
Re (refl+env+RM)
2.1 + 0.01
0.6
46.3 + 0.12
0.1
67.4 0.0
Table 10: Validation results the SaBBird-USA-Sumeen
Best results shown in bold. img refers to using the RGB true color image, refl refers to using RGBNIR reflectance bands, env refers using the environmental data (bioclimatic and pedologic variables). RM refers to the use of range maps.
Model
MAE[1e-2]
MSE[1e-2]
Top-10
Top-30
Top-k
Mean encounter rates
2.5 + 0.0
0.0
28.2 0.0
47.5 0.0
52.7
Environmental baseline
1.8 0.0
0.5
49.2 0.0
63.9 + 0.0
68.5 + 0.0
Satlas (img)
0.0
0.7
0.1
5..2
55.4
SatMAE (img)
2.0
0.7
29.7
0.1
54.0
MOSAIKS (img+env)
2.0
0.5
47.8
62.1 0.0
66.1
ResNet- 18 (img)
2.2
0.6
37.6 0.0
56.3 0.0
58.6
ResNet- (ref))
2.2
0.6
3.0
5.0
5.0
18 (img+env)
1.7 0.0
0.4
51.45
69.59
71.0
ResNet-18 (refl+env)
1.7 1.7
0.5
51.59
0.0
69.68 0.0
71.2
ResNet- 18 (refl+env+RM)
1.7+
0.4
51.55 + 0.0
69.61
0.0
71.2 + 0.0
Best results are shown in bold. img refers to using the RGB true color image, refl refers to using RGBNIR reflectance bands, env refers to using the environmental data (bioclimatic and pedologic variables). RM refers to the use of range maps.
Table 11: Validation results on the
20

---

Species scientific name
Training occurrences
MSE ranking
MSE
Calcarius lapponicus
7,46597932c-08
Geopelia striata
7.48139554c-08
Rallus obsoletus
3
7. 7,48282276c-08
Synthlioramphhs scrippsi
7.4867 -366-1366--8
Ardenna tenuirostris
5
7,48679727c-08
Calidris maritima
7,49105737e-08
Spizelloides arborea
7
7.491 -44144286--08 7.49201557e-08
Aethia cristatella
Table 12: Top predicted species the USA-summer test set according MSE. Occurrences refers to the number of training hotspots for which the encounter rate (target value) is non-zero.
Species scientific name
Training occurrences
MSE ranking
MSE
Agelaius phoeniceus
52630
670
0,09383779
Turdus migratorius
68198
669
0,07773756
Zenaida macroura
48582
668
0.07559232
Melospiza melodia
52107
667
0.06899277
Hirundo rustica
48299
666
0.06486867
Corvus brachyrhynchos
59977
665
0.0647007
Geothlypis trichas
38993
664
0.0632578
Cyanocitta cristata
47654
663
0.05804076
Cardinalis cardinaliss
48687
662
0.05713348
Passer domesticus
41578
661
0.05654651
Table 13: Bottom 10 predicted species on BatBird-USA-sumenn test set according to MSE in descending order. Occurrences refers the number of training hotspots for which the encounter rate (target value) is non-zero.
C.2 Analysis of results
On the test set of SatBird-SAA-summnn US--mmmmrr,, the top 50 species with lowest test-MSE are unsurprisingly species with very low number of occurrences in the training (< 11) (so the model can predict zero encounter rate and have good MSE for these species). Among those species we find species breeding in arctic regions (e.g. Bombycilla garrulus, Calcarius lapponicus) and seabirds (e.g. Uria lomvia). By design of our dataset, we don't 't have many observations of seabirds since we excluded all hotspots outside the boundaries of the continental US). Species with largest MSE have 30k hotspots where they were observed in the training set. Tables 12 and 13 show the best and worst predicted species on the USA-summer test set according MSE.
We show the squared error distribution by target value (encounter rates) for the USA -ummerr test set in Fig. 10 We binned the target values 19 bins and for each bin averaged the squared errors. The squared error is higher on average for higher target values. In Fig. [ we show the predicted values against the target values. The target values were binned in 19 bins. The model generally underestimates the target values.
We show the geographic distribution of the top-k error in the predictions for the USA-summer test set in Fig.
In Appendix we show sample predictions of our benchmark model (ResNet-18 18 with refl+cnv+RM) on the some of the rare species that are very habitat specific commonly found in certain areas, such as the Verdin, which is mainly restricted to serublands.
dataset. The example Fig. [2 shows that the model is able predict
D Results on Kenya dataset
HalBird-Kenyer dataset was created with the motivation of transferring model trained on large dataset SaaBBrdd-US-summeee for the task predicting species encounter rates to a region with less data available. We reported the baselines as done SatBird-USA.
21

---

Presiction vs Tarqet
0.8
Peediceed
6.2
00 002 611 A16 9.71 825 0.32 537
0.47 0.51 0.50 063 0.6 0.74 0.79 0.84 0.00 0.01 value
Figure 9: Predicted vs target values (binned in 19 bins) for the USA-summer test set.
Mean Recression Error by Target Value
0.5
Weon brgnesso
0.4
0.2
016 0.21 0.26 0.32 0.37 0.42
0.47 0:51 value
020
0:53
0.88
034 0.73
0.4 0.50
0.95
1.0
Figure 10: MSE value distribution by target value (binned in 19 bins) for the USA USA-summer test set. The bars show the minimum and maximum squared error for targets in a given bin.
Transfer learning: We experiment with transfer learning from models trained on SatBird-USA data to also experiment with freezing the weights and only update the last classification layer (freeze-USA) USA). All these models compared training SatBird-Keng Kenya from seratch with ResNet- 18. We report results for the SatBird- Kenya dataset in Table 14
low-data region compared to USA. With a Resnet-18 model trained on transfer the weights and fine-tune SatBird- Kenyaa (finetune- USA). We
Weighted Loss: In addition to the above experiments, we tried incorporating the number of complete checklists to the training strategy for Kenya. This was motivated by the observation that Kenya has has relatively fewer hotspots and some hotspots are over represented by the number of complete checklists reported. We address this by balancing the cross entropy loss by the number of complete checklists per hotspot. Our hypothesis that by balancing the cross entropy by number of complete checklists per hotspot, our model will learn a more balanced representation of encounter rates across the whole dataset. eBird, just like many other large biodiversity datasets may have biased estimates of species diversity, species occurrence generally follow log-normal
22

---

Adaptive Top-k accuracy
45
55
65
75
85
Figure 11: USA-summer test set hotspots colored by adaptive top-k performance for the ResNet-18 (ref+ten+RRM) model.
Because of this inherent bias, is unsurprising that the data distribution in Kenya, and parts of the USA will have fewer hotspots with complete checklists.
distribution with a long tail [56] 57 43 60)
We define the weighted loss, similar to our original loss, but with an additional weight as follows:
1 LWCE No E. Cn. N, Σx wh Estspeciss) -ii log(uk) - (1 vf,) log(1 - -%%
where: N₁. is the number of hotspots h. wh is the weight corresponding to the number of complete checklists for hotspot h. y are the predictions and y the ground truth encounter rates. The summation over represents a sum over all species.
In Table 14 we note that all ResNet-18 models perform similarly well with the use of cross-entropy as a loss function. One thing to note is that we did not perform any hyperparameter tuning so there is room for improvement for these baselines. Moreover, the sparse nature of the data and the large number of species (1054) to predict, which make this task particularly challenging might also contribute the limited performance of the models. ResNet- 18 initialized with the weights from the model trained on the USA data performed better on some metrics (MSE and MAE), highlighting the relevance of transferring knowledge from one geographical region and set of species to another. Satlas and SatMAE models seem to perform well overall. The MOSAIKS baseline performed the worst. A first path to improving this baseline would be experiment with different number of features generated from MOSAIKS. We also note that the images we considered were 64 64 with resolution 10m/pixel, when the original implementation was used for 256*256 images with 4m/pixel resolution.
As for using weighted loss in ResNet-18 baselines, it did not yield significant improvement to the performance the Kenya dataset. We think that this is result of the fewer number of complete checklists and hotspots overall in Kenya. The task extending these models to low-data regimes such in Kenya presents substantial and important challenge species distribution mod- elling. This task complex, and we think that would be one direction to expand upon in future work.
E Prediction samples
All predictions reported are those obtained with the ResNet-18 (refl+env+RM) model. We sampled randomly hotspots from the -SSBird-USS-smmmrrr test set and evaluated the model's performance in predicting the top- 10 bird species at each location. We selected hotspots from geographically distinct
23

---

Model
MAE[1e-2]
MSE[1e-2]
Top-10
Top-30
Top-k
Mean encounter rates
3.5 0.0
1.7
0.0
19.2 0.0
23.4
Environmental Baseline
3.8 0.0
1.00
10.6
18.7
24.2 0.1
Satlas (img)
3.1
1.7
12.6
29.5 0.2
0.1
SatMAE (img)
0.2
1.7
1..0
0.4
23.8
MOSAIKS (img+env)
4.8 0.0
2.1
8.9
15.2
19.7 0.0
ResNet-18(scrate)
3.9
1.7
+
35.1 +
28.6 0.4
ResNee--8(fmetunn--SAA)
3.
1.0
1..7
0.3
2.14
ResNec-18(freezzUU5A)
0.1
1.6
0.2
33.8
2.1
WL-ResNeet-B(<<ratch)
3.6::00
1.7
0.9
2.9 1.2
WL-ReeYeet-8Kfinttun--SSSS
3.6
1.6
2.5
33.0
27.4
WL-ResNNet-88ffreee---SS
3.6
1.7
16.6 1.5
+..4
26. +.3
Table 14: Test results on the SatBird-Kenyal img refers to using the RGB true color image. All ResNet-18 baselines use refl+env, which corresponds using RGBNIR reflectance bands and the environmental data (bioclimatic and pedologic variables), and WL refers to the weighted Loss. scratch refers training ResNet- 18 model from scratch. finelune-USA refers fine-tuning ResNet-18 model with weights transferred from SatBird- USA-summer. freeze-USA refers to using ResNet- 18 weights transferred from
while training the last layer only.
regions, featuring different land cover types, including the Pacific Coast (California), Northeast (Maine) and Central US (Ohio). The following figures illustrate the results from these regions.
Authors' statement
The authors bear all responsibility in case of violation of rights.
G
Licenses
The SatBird Dataset is released under Creative Commons AttrbbtioonNonCComerria 4.0 International (CC BY-NC 4.0) License (bttps://creativeccmmmoo orggss..gggiiii wrg.//rcnsees/////eoeeoeeeeeesee by-nc/4.0/). Additionally, use of our dataset should comply with the eBird Terms of Use (https://wog birds cornell. wwwnell..dnuaeeeeeeeeecc hadu/home/termssorfe -fseee/ and Terms of Use of eBird Status and Trends Products (https://scienn) products- access terms-of of usee)
laaadd.orr/e//ttatuus and-ttrrd-dd-trdedaa
24

---

Species
Predicted probsbilty
Species
Groundtruth probebilty
Ash-throated Flycatcher
Mouming
0.394
Aberts Towhee
Gambel's Quail
0.333
Verdin
0.5
House Finch
0.322
Mourning Dove
0.375
White- wenteewiinge
White-winged
0.284
House Finch
0.375
US-CA-L654261
Eurasian Colared Dove
0.241
Ladder-backed Woodpecker
0.375
Common Raven
0.239
Gambel's Quail
0.25
Turkey Vulture
0.239
Westem Screech-Owl Ow!
0.25
Black-talld Gnalccacher
0.184
Figure 12: Hotspot and species Desilt Wash, San Bernardino Couty in California: Our model correctly identified the Verdin in this region in the top-10 species predicted list. It's worth noting that the Verdin's 's habitat is fairly species whose habitat is mainly restricted shrublands.
Species
Predicted probabilty
Species
Groundrruth probabillty
American Robin
0.785
Song Sparrow
0.757
Red-winged Blackbird
0.753
American Robin
0.703
Song sparrow
0.727
Red-winged Blackbird
0.848
Gray Catbird
0.686
Canada Cocse
0.849
Northem Cardinal
0.676
Great Blue Heron
0.822
Festern Kinghird
US1CA3L1516413
Gray
Warbler
Mourinng Dove
0.481
Amadica Crow
0.476
Mourning
Figure 13: Hotspot and species Lampson Reservoir,Ashtabun County, Ohio: Our model predicts the presence of various species in this region, including the song sparrow, red-winged blackbird and gray catbird. These species are consistent with the ground truth and are highly reported on eBird for this location.
25

---

Species
Predicted probsbility
Species
Groundrruth prakablyyy
White-throated Sparrow
0.523
Northern Parula
0.878
Northern Parula
0.642
White-troated Sparrow
0.828
Rad-eyed Virea
0.526
Red- breasted Nuthatch
0.815
Black-eapped Chickadee
0.471
American Robin
0.785
Red-breasted
0.457
Blue Jay
0.764
Common
Black-capped
US-ME-L608588
Kinglet
Swainsor's Thrush
0.451
Bluehheaddd Vireo
Ovenbird
0.718
Magnolis Werbler
Veery
0.702
Figure 14: Hotspot and species in Kennebago River-Boy Scout Road, Maine: while our model identifies correctly the species with highest ground truth encounter rates, underestimates the encounter rates values.
26


Reviews:
[{"review_id": "JwZHKmapzc", "reviewer": "Reviewer_8dHr", "summary": "", "questions": "", "limitations": "- Need a better biological review and some validation of some data about bird habitat from the literature (not just citizen science). Also, some of the citations (e.g. 26, 28) seems out of scope with the main topic.\n- No information for specific bird predictions, limiting the interpretation of the results (e.g. is 46.2% accuracy good? What are we missing in the prediction?)\n- Miss some meta-data for the land use, land cover associated with the satellite imagery.\n- The included satellite imagery (Sentinel-2) is limited to 4 channels. \n- No clear explanation why Kenya was chosen in Africa, over other countries.\n\n", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Good agglomeration of different dataset \n- Good variety of tested machine learning models for the specific task.\n- Well written article, clear story, and good definition of the dataset task (in the main article and the appendix).\n- Code is well written with comments.\n \n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "5haOAZVR2Y", "reviewer": "Reviewer_Sbqv", "summary": "", "questions": "", "limitations": "I am unclear how reliable the ground truths estimates of the base encounter rates are. I have a passing knowledge of this area however am not familiar with the reliability of the estimates provided by eBirds or if there have been studies regarding that. It would be good if the authors could provide some resources regarding that, or point to methods the estimates account for observational biases.", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "I like the motivation of the dataset and the challenge of the task (modelling the species distribution of birds) the dataset is attempting to answer. It is clear that the authors have put in substantive amount of work in creating the dataset, and the approach developed by the authors will be helpful for inventorying  bio-diversity especially of species that are threatened. I also like that the dataset covers multiple geographic and seasons, and while it would be good to see a longitudinal study over several years to study the health of the conservation of the species. this dataset is a good start to build future datasets that can address that problem.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "y0cnjfOtN3", "reviewer": "Reviewer_rCBg", "summary": "", "questions": "", "limitations": "Yes, sufficient discussions are given.", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. The paper is well-motivated\nClimate change leads to biodiversity loss, impacting ecosystem services and human well-being. Understanding global species distributions is essential for policy decisions like land use and conservation. \n\n2. The proposed task is novel \nPredicting encounter rates of bird species using remote sensing and citizen science data is new and worth exploring.\n\n3. The evaluation metrics are well-defined", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "9GLna6RfQg", "reviewer": "Reviewer_ntto", "summary": "", "questions": "", "limitations": "- The article's corresponding documents available in the submission has some serious formatting issues. Submission introducing new datasets must include the supplementary materials (as one separate PDF); however, this article contains 3 different files as the supplementary material. \n- The submission should not include Appendix.\n- The supplementary file must include information about accessing the dataset so the authors should have all supplementary material in just one PDF file.\n- I consider the file Datasheet_SatBird.pdf as the supplementary material of the paper and review based on this file. The presentation of the dataset packages lacks clarity and completion. According to the package of the Kenya,  there are 8 dataframe (.csv), 1 text (txt), as well as 4 folders. These files and folders are not introduced individually with their names and details of their contents as well as some illustrations for example for the images. Therefore it is confusing when one extracts the compressed file. \n- Moreover, it is not clear what we should expect from each file or folder, for example, how the images look like as there are no samples of images and detailed descriptions of other files in the supplementary. When I opened images available in the folder \"images\" of the Kenya packages, I just found black images. Is that correct and should I expect this? Nothing is visible in these images!\n- These issues also makes the reproduciblity challenging and difficult.", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- The presentation of the idea and how/why this dataset is useful is clear and coherent.\n- Introduction and related works fairly address the main idea as well as the prior works done in the field.\n- The dataset is collected from more than one region (USA, and Kenya), from different seasons (summer and winter) and for a fairly good time period (13 years).", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "4ZuC2RAVHc", "reviewer": "Reviewer_d3tb", "summary": "", "questions": "", "limitations": "The author themselves address a set of limitations such as only using a single recent image per location, and only one per season. These are the major limitations of the constructed dataset.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper has several strengths, including:\n\n1) Well motivated and scoped. It is clear why this dataset is important (biodiversity monitoring) and how it differs from other datasets (joint modelling of bird distributions).\n2) Great use of domain knowledge by including several kinds of data modalities and by utilizing range maps from eBird to apply some heuristic domain knowledge when applicable.\n3) Evaluation across time of year and geographical location.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: The reviewers generally agree on the following strengths:

- this new dataset is well-motivated and has potential to have impact for biodiversity monitoring. 
- the dataset differs from existing datasets, e.g., joint modeling of bird distributions.
- it combines a wide range of times and geographical location, makes great use of domain knowledge, and has a good variety of tested machine learning models.
- the paper is well written, especially after more details were added during the rebuttal.

The reviewers had weaknesses that were generally addressed adequately during the rebuttal. For example,

- the authors added significantly more details and descriptions to various parts of their submission.
- added new experiments during the rebuttal: HPO for baselines, fine-tuning pretrained models, other loss functions, and have compared a baseline of using 20 single species models vs. one 20 species model.
- The only reviewer who gave a 5, was also the only one who did not reply to the rebuttal, having this review being is weighted less. I believe the authors’ response was adequate.
Decision: Accept (Poster)