WildfireSpreadTS: A dataset of multi-modal time series for wildfire spread prediction
Authors: Sebastian Gerard
Yu Zhao
Josephine Sullivan
Venue: NeurIPS 2023 Datasets and Benchmarks Poster (2023)
Keywords: remote sensing
satellite images
earth observation
computer vision
multi-temporal
climate change

Abstract: We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. The dataset is challenging due to: a) its inputs being multi-temporal, b) the high number of 23 multi-modal input channels, c) highly imbalanced labels and d) noisy labels, due to smoke, clouds, and inaccuracies in the active fire detection. The underlying complexity of the physical processes adds to these challenges. Compared to existing public datasets in this area, WildfireSpreadTS allows for multi-temporal modeling of spreading wildfires, due to its time series structure. Furthermore, we provide additional input modalities and a high spatial resolution of 375m for the active fire maps. We publish this dataset to encourage further research on this important task with multi-temporal, noise-resistant or generative methods, uncertainty estimation or advanced optimization techniques that deal with the high-dimensional input space.

WildfreSSpeeaTTS: A dataset of multi-modal time series for wildfire spread prediction
Sebastian Gerard, Yu Zhao, Josephine Sullivan
KTH Royal Institute of Technology 11428 Stockholm, Sweden {sgerard, zhao2, sullivan)(kth.es
Abstract
We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread resolution of 24 hours. The dataset con- sists of 13 607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains full time ries of daily observations, containing detected active fires and variables related fuel, topography and weather conditions. The dataset challenging due to: a) its inputs being multi-temporal, b) the high number of 23 multi-modal input channels, c) highly imbalanced labels and d) noisy labels. due smoke, clouds, and inaccuracies in the active fire detection. The underlying complexity of the physical processes adds to these challenges. Compared to existing public datasets in this area, WIL.FFRESPRRAAE ing wildfires, due to its time series structure. Furthermore, we provide additional input modalities and a high spatial resolution of 375m for the active fire maps. We publish this dataset to encourage further research on this important task with multi-temponne noise-resistant generative methods, uncertainty estimation advanced optimization techniques that deal with the nigh-dimensional input space.
allows for multi-temporal modeling of spread-
Introduction
With progressing climate change, the risk and severity of wildfires is expected to increase Shukla et al., 2019 Current active fire products Wooster et al., [021] that monitor such wildfires, use satellite-based observations to detect the current locations of wildfires, but do not predict their future spread. Regarding the use of modern deep learning in this field, much of the existing research focuses on detection, too. This includes work on early detection and continuous mapping of active fires Zhao let al.,,_022 well as the detection of burned areas Ban al., 2020 To go beyond detection and predict future fires, several datasets Sayad et al., 2019 Tavakkoli Piralilou et al., 2022 |Prapas et al.) 202 Prapas et al., 2022 Kondylatos al., 2022 have recently been published to estimate fire risk.
However, to fight an ongoing fire, predictions should be conditioned on the already existing fire, and have high temporal resolution. While some research exists in this area (see section 3), the topic of predicting the future spread of active fires is still under-explored and most existing papers do not publish the data they use. This lack of public datasets makes it harder for researchers who unfamiliar with the acquisition of remote sensing data to contribute their knowledge and skills. By releasing WILDFIRFSPREADTSS
want help bridge this gap.
For a fire to burn, three components are required: fuel, oxygen, and heat. How quickly and in which direction the fire spreads is governed by fuel conditions, wind, and topography. Relevant fuel conditions include the type and amount of fuel, as well as its humidity. This dataset a combination of existing data products that capture different aspects of all of these features, as well as the active fire masks indicating the fire's location. To determine the location of active fire, we use the VIRS active
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Fuel
Weather humiditty
Soe reluse conponent
eve
ren mertort
Topography
Weather forecast
Khal BrsCL aiddaaad
Dataset overview: The dataset's 's central task is to predict the next day's binarized active fire map (center box). based features for day are the day's active fire map and features related to fuel, topology and weather. All features were resampled to the spatial resolution of the actíve fire maps of 375m, and temporal resolution of 24h. Differences in the original spatial resolutions are clearly visible, for example the weather forecast data, which has resolution of roughly 27km.
Figure 1:
more days of input observations (outer boxes). The input
fire product Schroeder et al., 2014, which detects fires based satellite observations. The central task that the combined dataset represents is to predict the output of the VIIRS active fire product, as proxy for predicting the actual ground truth fire spread.
WILDFRESPREADTS is organized as a collection of image time series, with consistent temporal and spatial resolution. This structure makes it simple process with existing architectures, for example architectures designed for video data. We provide baseline results for predicting the next day's binarized active fire map, based either one day (mono-temporal) five days of observations temtti- The time series structure enables users to also construct different tasks, like predicting the wildfire spread several days in advance, predicting the duration of wildfire.
The only dataset we are aware of that represents wildfire spread predictions at similar resolution Huot et al., 2022 It proposes predict the next day's MODIS- -based active fire detections, based on mono-temporal observations at a resolution of 1km. Our work is heavily inspired by Huot et al., 2022 We discuss the differences to this dataset in seccion
Structure of this paper In /ectiion 2 we describe the dataset in detail, including dataset statistics and visualizations of dataset features. In sectionn 3] we describe related work in the field of wildfire spread prediction. We apply several deep learning architectures, described in section to establish baseline performances, which are reported in section 5] Finally, we suggest several directions of research that might improve predictions |section and indicate how to access the data in |section 7
Dataset
The WIRDAIRESPREADTS dataset consists 607 wildfire events in the contiguous United States from 2018 2021. The choice focus the United States was made solely based on the availability of data products, primarily the weather data from GRIDMET (the Gridded Surface Metcorological dataset). While we focus the US, hope this dataset will be useful in the development of new methods for deep learning-base regions of the world will then be important task beyond W WILDFIRESPREAD require addressing the additional challenges of generalization, and adapting to data sources with potentially different spatial and temporal resolutions.
wildfire spread prediction. Transferring such new methods to other
This transfer will
Since the source data products we use differ in spatial and temporal resolutions, we resample them to the 375m resolution of the active fire maps. This is done by Google Earth Engine internally, using

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bilinear resampling. In the VIRSS active fire product, several active fire detections are possible within one day. We aggregate them in 24h windows starting at midnight and resample all other data this temporal resolution. Figure I shows an overview over all features available for each day, grouped by feature category, as well as the original resolutions.
The following section provides detailed descriptions of how we created the dataset from the different original data sources. Afterwards, subsection 2.2 presents summary statistics. Finally, /ubsectton 2.3 illustrates how these statistics translate into real data with an illustrative example fire.
2.1 Detailed descriptions
This section provides details about the source data products, how and why we include them. The data processing described here is performed on the Google Earth Engine Gorelick et al., 2017 (GEE) platform. All of the used datasets are available GEE, except for the VIRS active fire product.
2.1.1 Selecting fire events from GlobFire
The core component of this dataset are the active fire maps, generated from the VIRSS Active Fire (AF) Oliva et al., 2015; Schroeder et al., 2014 product. This product only consists of individual coordinates of detected active fires, but does not combine them into separate fire events. To ensure that our dataset consists of fire events of relevant size, we consulted the GlobFire Artés et al., 2019 dataset. The GlobFire dataset provides the burned area polygons, and the start and end date of large number of wildfire events. We query the dataset for all fires larger than 1000 hectares in the years 2018 2021. For each of these, we extract features in rectangle of side length 1 in latitude and longitude around the center point. Due to the way GEE internally stores the data, the resulting images vary in size. To perform multi-tempporl predictions with a fixed-size input window, we also require observations from before the first day of the fire event. Therefore, we add four days before the beginning of the fire event in GlobFire. We also add four additional days at the end. We discarded any fire event which did not contain any active fire detections within the dates indicated by GlobFire and select few events for data format issues. All discarded events were relatively equally distributed across the years. In total, we retained 607 fire events and discarded 948.
The cause of the large number of events without active fire is likely the mismatch between the 500m MODIS burned area product MCD64A1 Giglio et al., 2022 which is the basis for GlobFire, and the 375m VIISS AF product VNP14IMG. This mismatch includes: a) the difference between burned area and active fire, b) conditions leading to false positives at 500m, but not at 375m and c) the temporal gap between when MODIS and VIRSS satellites observe the area.
2.1.2 Active fire maps
The VIIRS Active Fire (AFoOliva et al., 2015) Schroeder al., 2014 product is based on satellite observations by the VIRS sensor onboarding the Suomi National Polar-Orbiting Partnership (S-NPP) satellite. AF detection mainly relies the mid-infrared band 14 with wavelength 3.55-3.93 J m. It has 375m spatial resolution with two to four detections per day, depending on the latitude. We aggregate all the detections within daily 24h window into one active fire mask. If pixel is detected as active fire, its value is the latest time at which it was detected to be active. In Fiiure I] the different colors in the AF map indicate different detection times. The AF product also associates three-level confidence rating with each detection. We remove all low-confidence detections.
2.1.3 Detecting burned area, smoke and live fuel
The VIIRS Surface Reflectance product (VNPO9GA) Vermote et al., (2016] is based on the same satellite sensor the AF product. It provides one image per day with spatial resolution between 500 meters (Band I1-13) and 1000 meters (Band MI-M11). 1). We include bands 11, 12 and M11 because of their ability to distinguish healthy vegetation from burned areas and to detect clouds and smoke.
The widely-used vegetation indices NDVI and EV12 are computed the VNP13A1 VIISS Vegetation Indices Didan et al., 2018 product, based on 16-day windows of these observations. The product has spatial resolution of 500m and a temporal resolution of days. These indices provide more long-term information about living fuel than the surface reflectance data.
3

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Table 1: Data points per year: 2019 has un- usually few fire events, images, and active fire detections within these images.
Table 2: The persistence baseline predicts that the fire stays the same. We compare: a) using all fire detections of the day with b) using only the latest detections, on average precision (AP) and Fl score.
Image summary
all detections
last detection
Year
fires
imgs
% w/o detect.
Year
AP
F1
AP
Fl
2018
176
3773
39.8%
2018
0.187
0.432
0.121
0.317
2019
74
1425
48.9%
2019
0.105
0.323
0.086
0.279
2020
201
4292
40.8%
2020
0.194
0.439
0.144
0.345
2021
156
4117
35.1%
2021
0.287
0.535
0.189
0.392
mean
0.193
0.432
0.135
0.333
2.1.4 Weather data
The Gridded Surface Meteorological Dataset (GRIDMEETJAbazooglooo 2013 2013)comm consists of daily mete- orological data. We use its minimum and maximum surface temperature, total precipitation, wind speed and direction, specific humidity and the Palmer Drought Severity Index (PDSI) data. It also contains the energy release component, which relates to the energy released during combustion, and fuel moisture Bradshaw et al., 1984 The spatial resolution of the dataset is about 4.6 km. All of these features are the same as in Huot et al., 2022
2.1.5 Weather forecasts
The Global Forecast System (GFS Clough et 2005 provides hourly weather forecasts at spatial resolution of 27.83km. The forecast features are similar to those provided by GRIDMET, except that GFS forecasts the average temperature (instead of min/max) and the wind as wind speed in and direction. We recompute the wind into overall wind speed and wind direction, to be consistent with GRIDMET. GFS provides new hourly predictions four times per day. To harmonize these frequent predictions with our 24h aggregation window, we decide to use the 24 hourly predictions made by GFS midnight of the new day and aggregate all of them into one prediction.
2.1.6 Land cover and topography data
To understand the fuel type and topography of the surface, land cover and elevation data are included. For land cover data, the MODIS Land Cover Type Yearly Global product (MCD12Q1.0) [Sulla] Menashe et al., 2019; Friedl et al., 2022 offers 500m spatial resolution. We use the land cover types defined by the Annual International Geosphere- Biiesphee Programme (IGBP) classification criteria. For topography data, we use the NASA SRTM Digital Flevation dataset 2013 provide elevation, and derived therefrom, slope and aspect of the ground surface. These are relevant because with rising elevation, the oxygen level changes, fires move quicker up slope, but slower down slope, and the aspect indicates in which direction this effect takes place.
2.2 Dataset statistics
The dataset consists of 607 fire events in the years 2018 to 2021 in the contiguous United States, with a total 13607 daily images. Due to Google Earth Engine's 's internal processing, images vary in size, with sizes between (304 x 207) (smallest) and 356 x 308 (largest). The dataset is highly imbalanced, with about 0.1% 1% of pixels representing active fire, and the rest representing no active fire.
Table shows the aggregate number of fire events and images per year, as well as how many days 3) visualize the corresponding distributions over all years. The low number of 74 fire events for the year 2019 is caused by few candidate events in the GlobFire dataset, as well as containing many events that we had to discard. Even though we queried the GlobFire dataset for the whole United States, only fire events in the western part of the country are part of the dataset. This because most discarded events happened to in central US states or Florida. In the supplementary material, visualize the locations of included fire events map.
contain fire detections all. FFigure 2

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Days detections per event
140
Fre evena in eatsoot
120
120-
100
100
40
20-
20-
20
days
days
Figure 2: Some fires burn longer than others. The distribution of fire durations concentrated at 14 to 20 days with long tail that ends at 94 days. This includes the four buffer days, each before and after the dates of each fire event.
Figure 3: Not all images contain active fires. All fire events have at least one day with active fire detections, but many events have very few of such days. The histogram includes the four buffer days, each before and after the dates of each fire event.
Figure 4: Example active fire time series: The image shows an active fire time series from 2020. Colors indicate different detection times on the same day, acquired different overpass times by the VIIRS satellites. The time series was cropped to the 128 x 128 area that contains the most fire.
2.3 Example active fire time series
Figure shows example time series from 2020, highlighting key characteristics and challenges of the dataset. The first challenge is the difficulty of predicting the physical progression of fires. Many fires consist only of few AF detections without any larger fire clusters (first row). Some fires then suddenly ignite into a larger fire and progress in a way that is hard to predict (second row). Other fires grow and progress more slowly and predictably. The second challenge relates labelling noise. In Figure 4| we see the four buffer days at the start and end of the sequence contain AF pixels even though GlobFire reported the fire was active during these times. In contrast, within the official fire dates there are time-steps containing no AF detections.
3 Related work
In this section, we first give a broad overview of existing work in wildfire spread prediction, and then delineate how work differs from the closest existing one.
Simulation software: In practice, wildfire spread prediction performed with simulation models like FARSITE Finney, 1998 or Prometheus Tymstra et al., 2010, Such simulators have been used to generate training data, and train neural networks to approximate the spread predictions made by
the simulators Hodges et al., 2019
Burge et al., 2022 Bolt et al., 2022 A big advantage of these
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methods is that they can generate data at high resolutions, for example (30 meters x 15-30 minutes) in Burge et al., [2022] Bolt et al., 2022 However, such data is not readily available for real-world situations and the simulator-basee modeling might contain errors that prevent them from working optimally on actual satellite observations. Bolt et al. therefore suggest as future work to pretrain models on simulated data, and then transfer them to real-world observations.
Tabular data:, Singla et al. published WILDFIREDB, a wildfire spread prediction dataset in the form of tabular data, predicting whether a burning cell will spread its fire to a neighboring cell. In comparison, our dataset adds both a spatial and temporal dimension, enabling methods to be used that explicitly model these relationships, e.g. computer vision methods.
Computer- vision: Radke al.; Khennou et al. and Subramanian et al. use Landsat 7 or 8 images, that have a high spatial resolution of 30m, but only provide an image every 8 days, assuming no cloud- or sovke-cover. In contrast, we use daily observations. First results in [aakee et al., 2019 Ntinas et al., 2017] Ross. [202T indicate that deep learning methods can sometimes beat FARSITE, motivating further research on learning directly from observational data.
Daily multi-temporal input: For the special case of Canadian peat fires, Bali et al. publish a dataset of predicting the burned area. Huot et al. investigate the problem of using one day's observations for predicting either the next day's 's fire or the aggregated fires over the following week, as well as using a week-long time series of observations predict the aggregated burn area. The multi- temporal dataset used in Huot et al., [202] has not been made available to the public, to the best of our knowledge. In Huot al.. 2022 corresponding mono-temporal dataset is published, called It focuses on the task of predicting the next day's wildfire spread,
NEXTDAY WIL.DIR based single day of input data. A variant of the dataset has been published in the context of disaster response hackathon Jeffrey et al., 2023 It uses the same VIRS active fire product that we use, but is otherwise similar NEXTDAY WILDIRERA
3.1 Comparison to WextDay-WildfreSppead
Since Huot et al. already published a mono-temporal dataset for wildfire spread prediction, we adopted many of their choices when creating this dataset. In this section, we discuss the important differences between their datasets and ours.
Time series structure NEXTDAY W WILDFIRESPREAD consists of input-output pairs of single-day observations and targets. In contrast, WILDFIRESPREADTS consists of 607 multi-day time series, each associated with an individual fire event. This structure allows the construction of not only multi-day inputs, but also custom task definitions, like predicting active fire one seven days in advance, or predicting the duration of a fire. This extension of the data into the temporal dimension, which enables multi-temporal modeling, is the core contribution of our paper.
More recent observations To describe the state of the vegetation, Huot et al. use the vegetation index NDVI, which is updated every 8 days. We add to this the daily VIRS reflectance observations, to receive more frequent updates about vegetation conditions. This seems especially important in the context of longer-lasting fires, where using data that is seven days old might lead to sub-par results. Together with moving to a multi-temporaa setup. the VIRS observations provide the model with more up-to-date information, thereby potentially improving predictions.
Additional input features We add weather forecast data, which seems vital to predict whether a fire is going to be extinguished by heavy rain, or whether the flames will be fanned into a certain direction by strong winds. Since the direction and speed of fire spread is also impacted by the local topography, we add aspect and slope as features. We add land cover classes to differentiaae fuel types and detect the presence of fire-impeding land covers, like barren or built-up land, or water bodies.
is based on MODIS observations with spatial resolution of 1km. All features are resampled to this resolution. As we use VIRS active fire labels, we resample all data to 375m, instead. This increased resolution in our dataset leads to increased resolutions for topographical features, vegetation indices, surface reflectance and land cover, which might positively affect predictive performance. We also provide more active fire masks. Since VIRS active fire detections occur 2-4 times per day, we indicate for each pixel the hour at which it was last detected as burning. In In/seciion 5) we predict the next day's 's fire as
Fire masks
WILTDAYWILDFFRESPPEE
temporal information for the input

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binary label. However, the temporal information included in the active fire masks could also be used predict sub-daily wildfire spread. by predicting the detection times.
Due to the already large file size, caused by the number of additional features, we currently only use data from 2018 to 2021, while Huot al. use data from 2012 2020. We also do not include proxies for anthropogenic activity, since these features are mostly important for predicting the risk of ignition, as far we are aware, while we care about the situations in which ignition has already occurred.
Method
To establish performance baselines, we apply several widely-used architectures to our dataset.
Logistic Regression Huot al. use logistic regression with 3 x 3 input window as a simple baseline model. We implement this architecture as as a single convolution with a 3 x kernel. We only use mono-temporal data for the logistic regression.
U-Net We use U-Net Ronneberger et al., 2015 based on ResNet18 He al., 2016 architecture. for both mono- and multi-emmpoaal data. Since it has no direct way to handle multitemporrs data, we concatenate all time steps the channel dimension.
ConvLSTM The ConvLSTM [Shi et al., 2015 a recurrent neural network architecture based on convolutional layers and able to process multi-tempont image data. We single ConvLSTM block, followed by a final convolution that produces the segmentation map.
U-Net temporal component Several architectures exist that combine the U-Net architecture with a temporal component. We use version called UTAE Garnot et al., 2222 that applies a simplified multi-head self-attention across the temporal dimension the bottleneck. The generated attention map is then upscaled and applied to all skip-connections between encoder and decoder.
Experiments
Software & Hardware All of our models, experiments, training procedures and data handling are implemented in Python using Py PyTorc Paszke et al., 2019 and Lightning Falcon et al., 2019, and logged via Weights and Biases Biewald, 2020 We used NVIDIA A40 GPUs situated in the NAISS cluster Alvis (see acknowledgements), The experiments reported here, including hyperparameter searches, took 1278 hours. With debugging, re-runs due to errors etc., computations took about four times as long.
5.1 Model and training hy typeraarameters
Unless mentioned otherwise, we use the default hyperparameters of the respective implementations. Loss functions and learning rates are chosen via coarse grid searches. Please see the supplementary material for details. We always use the PyTorch implementation of the AdamW optimizer Loshchilov 0.01. We keep the model with
et al., 2019 with the default parameters 81 0.9, B2 0.999, the highest validation loss, evaluated after each training epoch and train for a total of 10k steps.
For the logistic regression, we use Dice loss with learning rate 0.1. For the U-net, we use the SMP implementation lakubovskii with ResNet18 backbone and train it with Dice loss with learning rate 0.001. For SSM and UTAF, we use the implementations VSainteuf, 22033 by the Garnot et al., 2022 authors. For CSTVLSTM, we use Jaccard loss with learning rate 0.01. For UTAE, use weighted cross-entropy and learning rate 0.01. The weight for the fire class is the inverse relative frequency of the fire class in the used training set.
5.2 Dataset preparation
We split the data into train/val/test by using two years for the train set and one each for validation and test set. For all experiments, we perform a full 12-fold cross-validation over all possible permutations of years assigned train/val/test set. This is necessary, because of the large differences between the yearly data distributions (see Table I and Table 2 more details in the supplementary material).

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For fair evaluation, we need to ensure that the test data is the same across all experiments. However, the number of data points per fire depends on the size of the temporal window w,, i.. the number of observed days used as input. E.g. for wi 5, we would use the first five days as input, and the binary fire mask of the sixth day as the first target, while for wt = 1, we would use the second day as our first target, leading to differences in the test set. The reason for this discrepancy is that we only use real data as input to our models, since it is realistic to have access to past data, instead of padding the input with empty frames. In our experiments, we use maximum Wy 5. meaning that the first target used in evaluation belongs to day six. For evaluation on the test set to be fair across different values of Wt, we have to leave out initial data points in experiments with wt 5, to ensure that all testing starts on the target of day six, for each fire. During training, we instead use all of the data. For details on preprocessing and augmentations, please see the supplementary material.
To evaluate models, we use the test set average precision (AP). It summarizes the precision-recat curve in single number and is preferable to metrics like AUROC in cases of imbalanced datasets, like WIL.FIRRESPEFADDS Compared to the F1 score, it considers all possible thresholds instead of just one. For the persistence baseline, we still provide the Fl score, to give readers who are more familiar with this metric an intuition of how good the corresponding AP scores are.
5.3 Fire persistence baseline
As simple baseline, we use fire persistence, as in HHuo et al., 2022 same active fire for tomorrow as was observed today. Table 2 shows that the performance of this baseline varies strongly between the different years. Together with the lower number of fires for 2019, this indicates that the difficulty of predicting wildfire spread might vary accordingly.
consists of predicting the
Since our data includes the time which each active fire pixel was last detected, we can construct a more tee-ggrined variant of this baseline, using only pixels from the last detection of the day. If a pixel was detected as active fire early in the day, but not in the last detection of the day, it might have stopped burning. The results in Table 2 however, show a worse performance for this -rainne baseline. The causes could be: The later detections did not cover the exact same area as the earlier ones, thereby leaving out some burning pixels that were out of view. 2. The detections are noisy and might fail to recognise some of the pixels as burning. This could be caused by different view angles between observations, or temporal variations in fire intensity or cloud/smoke cover.
5.4 Ablation studies
To investigate how useful different features are, we combine the active fire masks with different feature groups. We train the U-Net on the resulting feature set, varying the numbers of leading days of observations. In preliminary experiments, we found that the U-Net is very easy to optimize, even though it does not explicitly model the temporal dimension. Explicit difficult to optimize in some cases, which is why we use the U-Net for the ablation studies.
models proved
A Ablation:
Vegetation Table 3 compares the common vegetation indices NDVI and EV12 with the daily VIIRS reflectance product that we add in this dataset. For one and two days, VIRS performs better than NDVI, which was the best feature solution in Huot et al., 2022 Table V. For three to five days of input observations, VIRSS, NDVI and EV12 perform similarly, although the exact ranking fluctuates. This strong performance of VIISS on shorter time series might be caused by the higher temporal resolution, getting daily updates instead of every eight days, and VIIRS bands also offering information about the presence of clouds and smoke. Observing multiple leading days also improves performance for all vegetation features, validating the addition of the temporal dimension.
Ablation: Non-vegetation In Table we test all non- vegetaattn features in the same setting. We see that topography features perform best, though still worse than the VIIRS features. Again, increasing the number of observed leading days increases the model's performance. Notably this is also true for static features like topography.Thir This indicates that the additional active fire observations contribute some of the improvement, possibly by indicating already burnt areas. Each combination of fire mask and individual feature group and outperforms the persistence baseline that reaches a mean AP of 189. Even the worst performing result this ablation study, ERC & drought, with a single input observation, has a mean AP of 0.291. We also observe that performance can decrease when adding more time steps, even though the model could just learn to ignore the additional data and retain its performance. This could be an issue of optimization difficulty in high-dimensional feature spaces.
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Table 3: Ablation: Vegetation We compare EVI2, NDVI and VIRSS reflectance as sources for vegetation data, combined with fire masks. The models predict the wildfire spread 24h ahead, based on one to five days of observations. The performance displayed is the mean test set average precision + the standard deviation, computed in a 12-fold cross- voos-validation over years. Higher is better.
Features
Input days
EV12
NDVI
VIIRS
0.302 = 0.096
0.298 + 0.091
0.314 + 0.093
2
0.313 0.091
0.314 0.085
0.319 = 0.087
0.321 # 0.092
0.318 + 0.087
0.320 = 0.085
4
0.319 0.084
0.325 = 0.087
0.323 + 0.091
5
0.318 + 0.085
0.319 = 0.080
0.319 + 0.087
Table 4: Ablation: Non-vegetation We compare all non-vegetation features, individually combined with the fire masks imput features. The models predict the wildfire spread 24h ahead, based one to five days of observations. The performance displayed the mean test set average precision + the standard deviation, computed in 12-fold valss-valiittion years. Higher is better.
Input days
Features
ERC, drought
Landcover
Topography
Weather
Weather forecast
1 0.291 + 0.088
0.301 = 0.094
0.306 + 0.087
0.295 + 0.091
0,296 + 0.098
2
0.300 0.088
0.311 = 0.087
0.315 = 0.087
0.308 0.089
0.296 0.094
3
0.303 0.096
0,314 = 0.084
0.317 = 0.082
0.316 + 0.087
0,292 = 0.103
4
0.308 0.087
0.307 = 0.084
0.321 = 0.089
0.313 0.093
0.289 0.094
5
0.313 + 0.086
0.318 # 0.086
0.317 + 0.080
0.308 + 0.091
0.293 + 0.096
Table 5: Baseline results The models predict the wildfire spread 24h ahead, based of observations and different input features. Multi includes vegetation, land cover, topography and weather features. The performance displayed the mean test set average precision the standard deviation, computed in a 12-fold cross-validation over years. Higher is better.
five days
Features
Parameters (All)
Model
Input days
Vegetation
Multi
All
Persistence
0.193 + 0.065
0.193 0.065
0.193
0.065
Log. Regression
0,279 + 0.092
0.288 0.091
0.286 + 0.092
361
Res18 U-Net
1
0.328 # 0.090
0.341 : 0.085
0.341 0.086
14.4M
Res18 U-Net
5
0.333 + 0.079
0.344
0.325 0.108
14.7M
ConvLSTM
5
0.306 + 0.082
0.310 + 0.085
0.292 + 0.094
240K
UTAE
5 0.372 # 0.088
0.350 0.113
0.321 0.135
1.1M
Ablation: Combining features To assess the cumulative contribution of these features, we sequen- tially combine features. Testing all feature combinations for all time windows is computationally prohibitive. Instead, we sort feature groups in descending order w.r.t. performance on the ablation experiments reported in Table 4 and then add them cumulatively one by one. To reduce experiments, we group all vegetation features together and only compare time windows of one and five days. Table 2 in the supplementary material shows the results in detail. The performance does not always improve with more features or input days. Instead, more features or days sometimes lead to worse results. This an issue of optimization, possibly made more difficult by the higher input dimensionality, since the model could theoretically just ignore the additional inputs. The best performance is achieved by mod- els using vegetation, land cover, topography and weather data, with five days of input observations. Adding weather forecast, ERC and drought results in worse performance.

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5.5
Baseline results
To investigate how different baseline models perform on the dataset, we train them on: a) the best one-feature setting from [able 3 and Table 4] b) the best multi-featur setting (Table 2 in the supplementary material) and e) all available features. The results are displayed in Table 5]
The best result by far is achieved by the spatio-temporal UTAE method, with five input days, using only the vegetation features. This validates moving from mono-temporal methods, that only model spatial interactions, to multi-temporal methods, that model spatial and temporal interactions. The UTAE achieves maximum AP of 0.372 on the vegetation features (plus the fire masks which are always included). Adding more features reduces the performance to 0.350 for the multi-feature setting and to 0.321 when using all features in the dataset. This hints at optimization difficulties in high-dimensionn feature space, similar to what we observed in the ablation studies.
The next- best model is the U-Net with maximum AP of 0.341 for the mono- mono-temponn temporal and 0.344 for the multi-temporal settings. Notably, the UTAE has about 13 times fewer parameters than the U-Net, but outperforms it by up to 3.9 pp. The U-net does outperform the ConvL.STM, the second spatio-temporal model we tested, which reaches a maximum AP of 0.310. However, the ConvI. also has 61 times fewer parameters than the U-Net. The logistic regression provides strong baseline, achieving a maximum AP of 0.288. All of the parametric models very clearly beat the parameter-free persistence baseline that has an AP of 0.193, showing that they do indeed learn meaningful features.
Opportunities
In this paper, we introduced Wildfire WildfireSpreadTS, a dataset dedicated to predicting the spread of wildfires based on time-series data. We believe that a wide variety of methods are promising avenues to improve the state of the art of predictions on this important problem.
Optimization We have shown that parameter optimization with this high-dimensional, multi-modal dataset can be difficult. While we used standard techniques, more sophisticated methods for opti- mization with high- nigh-dimensional data could improve upon these issues.
Self-supervised pretraining Self-supervised learning (SSL) has proven to be useful for satellite observations Ayush et al., 202T Gerard et al., 2022 Cong al., 2023 Additionally, Kang et al., T found that SSL. pre-training is beneficial for class inssliibbl datasets. Applying this pretraining could alleviate the optimization difficulties that we encountered.
Noisy labels Since the active fire detections are impacted by various noise sources the acquisition process, using methods that are tolerant towards label noise SSnn et al., 2022 Englesson al.. 202T could improve the robustness of results. Alternatively, uncertainty estimation Gawlikowski al., 2022 or generative modeling could be approaches deal with the fact that the process of fire spreading likely contains some stochasticity at the resolution that we are working with.
Separating static and dynamic f'eatures While this dataset is structured such that every day provides all features, some of them do not actually change on a daily basis. Treating these static and dynamic features separately, as done by Eddin et al., could facilitate the optimization problem.
7 Access to the dataset
The most up-to-date information on the dataset, as well as the code used this paper, can found at oihhtps: : //t.tttt.bbb...t/ com/ SebastianGer / Wi//iildll Wf rSSpreadTS) This includes a Py PoToc dataset class, and Lightning data module. Our code is available under the MIT license.
The full dataset Gerard et al., 2023 of about 50GB is available as a collection of GeoTIFF files under the CC-BY-4.0 license at httpthtpt:///d http://ooo..oog....// 5281/zenoda 8006177 For training, we recommend converting the dataset to HDF5. The corresponding code is available in our repository.
10

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Acknowledgments and Disclosure of Funding
This work is funded by Digital Futures in the project BO-AL4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.
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15


Reviews:
[{"review_id": "KsJYez1umc", "reviewer": "Reviewer_n9fn", "summary": "", "questions": "", "limitations": "Limitations are discussed throughout the paper, and at the end in the \"Opportunities\" Section. But most of these focus on modeling limitations. I was expecting authors to discuss any issues that might arise from interpolating input weather data to the spatial resolution of the active fire maps. Going from 27km to 375m of spatial resolution can lead to significant noise in the input data and little detail is provided what methodology was used for this downscaling. This can be a source of noise in the dataset and as much as weather data is essential for this prediction problem, if the quality of this downscaling is low it might have a negative impact. ", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "The dataset improves the state-of-the-art existing training data for wildfire mapping by adding new predictors such as weather conditions and topography. Providing the data in consistent formats and enabling the user to choose different temporal windows for predictors and predictions is great. The paper very well explains the issues/gaps with existing training datasets, and address those. Authors have selected the best of available satellite and weather observations to construct the dataset. ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "G0BZWOfSlp", "reviewer": "Reviewer_hHGN", "summary": "", "questions": "", "limitations": "I believe the authors have adequately addressed the limitations and potential negative impacts.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "- Integrating the wildfire data with other climate variables is not an easy task and it is time-consuming. Therefore, it is nice that the dataset includes other weather and climate variables like wind speed, min and max temperature, precipitation etc.\n- The authors recommend (and provide code) that the data be converted to HDF5, which is a versatile, interoperable file format that is supported across the platforms and programming languages", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "HrjiZukIZY", "reviewer": "Reviewer_ANKW", "summary": "", "questions": "", "limitations": "please see the last question", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "•\tThe authors have taken a comprehensive approach, recognizing the significance of long-term wildfire prediction. They have collected relevant data before and after the wildfires and increased the spatial resolution of the wildfire data.  This enables developers to build multi-day inputs and customize prediction tasks effectively.\n•\tThe authors have released this dataset to encourage researchers to further investigate wildfire propagation using methods such as multi-temporal analysis, noise reduction, or generative techniques\n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "z6PohwD26C", "reviewer": "Reviewer_KxL4", "summary": "", "questions": "", "limitations": "The authors are clear in the limitations of their work, especially for the experimental section.", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "The paper is focused on a very important and relevant topic in view of climate change. It provides an harmonised dataset of wildfire drivers\nto perform active prediction, using multi-temporal wildfire drivers as input. Applications based on this dataset have the potential to provide actionable knowledge to emergency responders and civil protection authorities. In addition, it can provide valuable insights to the scientific community on wildfire behavior and spread patterns. The dataset has also the potential to study the evolution of extreme wildfire events. \nA nice feature of the dataset is the inclusion of weather forecasts, not just reanalysis meteorological data. In this way distribution shifts between training and production can be avoided.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "g7YGLyDhFs", "reviewer": "Reviewer_3tFe", "summary": "", "questions": "", "limitations": "I appreciate the authors’ practice of making any encountered problems or limitations very explicit, to the best of their knowledge. It is unfortunate that I do in fact find some of the outlined issues problematic, but having them fairly exclaimed before any reviewer first brings them up hopefully makes the review process more agreeable for all involved parties, and maybe the responsible chairs want to take this into account in a suitable manner. In particular, here's two limitations mentioned before, which I'd like to highlight in this text box:\n\n> [...] the reported results are overly mixed and make me wonder about the suitability of the chosen baselines---or alternatively, the appropriateness of the curated data and the proposed task. Generally encountered optimization problems are acknowledged in section 5.4 of the main text, while the performances of some baselines may also be affected due to model-specific experimental design choices („likely influenced by how we have to compute test set predictions for this specific architecture, using crops of padded images to follow the image size requirements prescribed by the architecture.“ (p.8, l.272-274)). Overall, this seems to me like part of the baselines may have been chosen or handled unfortunately, but it is challenging to rule out further issues.\n\nThis is the most apparent issues I'm having with assessing this submission. As stated by the authors in section 5.4 of the submission, the proposed dataset poses several challenges (heterogeneous multi sensor data, high dimensional input space, time dimension on top) that make it difficult to optimize established baselines upon. One potential way of interpreting this issue is to conclude that a broad range of existing methods are not suitable for the submitted dataset and hence further methodological development is needed. However, I think this would be oversimplifying the problem and that there's still need for the proposed dataset to prove itself: notably, parts of its significance is due to more data modalities (which may cause the high-dimensional issues and result in poorer optimization than using fewer modalities) and the time series extension (which oftentimes causes optimization problems and yield mixed results). The drawbacks encountered at the cost of introducing these novelties make it challenging to fully appreciate them. It is challenging to propose any solution to this issue, but I think one initial step would be to run ablations determining the benefits of the new modalities individually, plus considering shorter time series of lengths 2, 3, 4 and analyzing at which point the benefits of additional data are offset by the challenges of optimization.\n\n>It is my understanding that (parts of) the benchmarks are based on a digital elevation model that may not be accessible in the dataset release, which raises concerns about the reported outcomes being faithfully reproducible. Concretely, the main text states „Due to a misunderstanding, the experiments in this paper use the SRTM90_V4 product Reuter et al. [2007] by CIAT as the source of topographical data. The published dataset contains the NASA SRTM V3 product instead“ [p.3, l.145-147]. I appreciate the authors’ transparency and see no strong reason to doubt their estimate this wouldn’t significantly change the reported outcomes. However, the discrepancy of data being used in the paper versus data being released in the dataset makes me wonder whether the reported results would still be reproducible, which may be considered an issue. \n\nThis is an issue which should ideally not occur in a submission to a selective venue, but I'd not consider it a reason for rejection on its own. As suggested above: Alternative to re-computing the experiments based on the correct elevation maps, would it be feasible to also release the utilized SRTM90_V4 with the final dataset? I could imagine that future users of the dataset would rather prefer to faithfully stick to the data as utilized in the experiments, so they may be more confidently comparing their results with the reported outcomes. I think that addressing this issue, at least to some extent, shouldn't be considered totally impractical.", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "### Significance:\n* Wildfires are devastating disasters of increasing frequency, whose occurrence should be mitigated. This submission proposes a dataset for forecasting wildfire spread with time series of up to five days, whereas most prior work thus far focused on live detection or single day time horizons. I consider the extension of the temporal component is a meaningful improvement over prior work.\n* The submission showcases its proposed dataset by benchmarking five fundamentally different models, plus a total of 10 variations thereof: two simple approaches, three convolutional models with a U-Net backbone, three LSTM networks and two attention-based models based on U-TAE. The different variants are designed to investigate the benefits of multi-temporal data, plus the opportunities and challenges of utilizing high-dimensional multi-sensor information for wildfire spread forecasting.\n\n### Relevance:\n* The occurrence of wildfires increased over the past few years and is prognosed to rise further. Topicwise, this makes wildfire monitoring an important matter. Accordingly, the subject has grown of scientific relevance recently, e.g. with a handful of accepted papers on the topic at last year’s [NeurIPS Workshop on Tackling Climate Change with Machine Learning](https://nips.cc/virtual/2022/workshop/49964). My impression is that a growing extent of the core machine learning community becomes interested in AI for social good and, per extension, utilizing satellite data for such purposes.\n\n### Accessibility and Accountability:\n* The proposed WildfireSpreadTS dataset will be released on Zenodo, which is a common and accessible choice for open data sharing in the scientific community. The dataset is only released officially upon acceptance, but the authors share a private version with the reviewers, plus the provided Datasheet for Datasets gives a good overview of the curated dataset. \n* Moreover, I appreciate the authors share both their code for [using the dataset and baselines](https://github.com/SebastianGer/WildfireSpreadTS) as well as for [re-creating the WildfireSpreadTS dataset](https://github.com/SebastianGer/WildfireSpreadTSCreateDataset). The latter may also be beneficial for the community to modify and extend the proposed data, e.g. to different geospatial territory.\n\n### Ethical and social implications:\n* Research on a timely forecasting of wildfire spread may in the future yield better predictions and improved early warning systems, whose social implications would be clearly positive.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: * Summary. The manuscript introduces a valuable multi-temporal, multi-modal dataset for predicting wildfire spread. It contains 13,607 images from 607 fire events, providing a rich resource for research. The dataset's time series structure enables in-depth analysis of wildfire dynamics and includes diverse environmental variables. Challenges include multi-temporal inputs, many input channels, imbalanced and noisy labels, and the complexity of physical processes. Despite these challenges, the dataset offers opportunities for advancing wildfire prediction methods and encouraging research in noise-resistant and generative techniques.
* On reviewer’s comments. Reviewers' opinions on the paper vary. Reviewer 1 rates it 8 and strongly recommends acceptance, lauding the new wildfire prediction dataset and suggesting improvements. Reviewer 2 gives it a 7 and recommends acceptance, appreciating the climate variables but pointing out the need for better documentation and dataset expansion. Reviewer 3 rates it 4 and suggests rejection due to concerns about additional features and data resolution. Reviewer 4, with a confidence rating of 5, strongly recommends acceptance (rating 7) and praises the dataset's significance. However, it offers several suggestions for improvement, including making it ML-ready and conducting experiments without reflectance data. Reviewer 5 initially rates it marginally above acceptance (6) but raises their rating after revisions, expressing concerns about mixed baseline results and dataset design. They stress the need to demonstrate the significance of new features and suggest alternative evaluation setups for reproducibility. Overall, the paper obtains quite uneven ratings and feedback, with some reviewers highlighting its importance and others offering substantial improvements and clarifications. 
* Author’s responses. The authors engaged in a fruitful and constructive discussion and provided convincing explanations to the reviewer’s comments, addressing most of their concerns. After the multiple review comments, the general feeling is that most problems have been discussed competently, and one reviewer even upgraded the initial rating.
* On this work's quality, clarity, originality, and significance. The work is of high quality, offering a valuable wildfire prediction dataset. However, clarity could be improved, and some reviewers noted concerns about dataset features and baseline results. While the paper's originality lies in its dataset, addressing these concerns is essential for its significance to the wildfire research community.
Decision: Accept (Poster)