IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain

• URL: http://arxiv.org/abs/2505.00837v1
• Date: Thu, 01 May 2025 19:54:17 GMT
• Title: IberFire -- a detailed creation of a spatio-temporal dataset for wildfire risk assessment in Spain
• Authors: Julen Ercibengoa, Meritxell Gómez-Omella, Izaro Goienetxea,
• Abstract summary: Wildfires pose a critical environmental issue to ecosystems economies, particularly in Mediterranean regions such as Spain.<n>To address the lack of localised and fine-grained data, this work introduces IberFirecube at 1 km x 1 km x 1-day resolution from December 2007 to December 2024.<n>IberFirecube supports advanced wildfire risk analysis through Machine Learning (ML) and Deep Learning (DL) techniques.<n>The dataset is publicly available on Zenodo to promote open research and collaboration.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wildfires pose a critical environmental issue to ecosystems, economies, and public safety, particularly in Mediterranean regions such as Spain. Accurate predictive models rely on high-resolution spatio-temporal data to capture the complex interplay of environmental and anthropogenic factors. To address the lack of localised and fine-grained datasets in Spain, this work introduces IberFire, a spatio-temporal datacube at 1 km x 1 km x 1-day resolution covering mainland Spain and the Balearic Islands from December 2007 to December 2024. IberFire integrates 260 features across eight main categories: auxiliary features, fire history, geography, topography, meteorology, vegetation indices, human activity, and land cover. All features are derived from open-access sources, ensuring transparency and real-time applicability. The data processing pipeline was implemented entirely using open-source tools, and the codebase has been made publicly available. This work not only enhances spatio-temporal granularity and feature diversity compared to existing European datacubes but also provides a reproducible methodology for constructing similar datasets. IberFire supports advanced wildfire risk modelling through Machine Learning (ML) and Deep Learning (DL) techniques, enables climate pattern analysis and informs strategic planning in fire prevention and land management. The dataset is publicly available on Zenodo to promote open research and collaboration.

Related papers

• Advancing Eurasia Fire Understanding Through Machine Learning Techniques [0.0]
  We present one of the most extensive datasets available for wildfire analysis in Russia, covering 13 consecutive months of observations.<n>We conduct exploratory data analysis and develop predictive models to identify key fire behavior patterns across different fire categories and ecosystems.
  arXiv  Detail & Related papers  (2025-02-24T10:22:17Z)
• Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset from Multisource Observations [0.0]
  This study introduces a novel and comprehensive dataset specifically designed for wildfire prediction in Morocco. We compile essential environmental indicators such as vegetation health (NDVI), population density, soil moisture levels, and meteorological data. Preliminary results show that models using this dataset achieve an accuracy of up to 90%, significantly improving prediction capabilities.
  arXiv  Detail & Related papers  (2024-11-09T15:01:12Z)
• FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2 [41.28284355136163]
  Wildfires pose significant threats to human and animal lives, ecosystems, and socio-economic stability. In this work, we create and introduce a machine-learning ready dataset we name FLOGA (Forest wiLdfire Observations for the Greek Area) This dataset is unique as it comprises of satellite imagery acquired before and after a wildfire event. We use FLOGA to provide a thorough comparison of multiple Machine Learning and Deep Learning algorithms for the automatic extraction of burnt areas.
  arXiv  Detail & Related papers  (2023-11-06T18:42:05Z)
• Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean [5.100085108873068]
  Mesogeos is a large-scale dataset for wildfire modeling in the Mediterranean. It integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks.
  arXiv  Detail & Related papers  (2023-06-08T12:11:16Z)
• Federated Prompt Learning for Weather Foundation Models on Devices [37.88417074427373]
  On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing. This paper propose Federated Prompt Learning for Weather Foundation Models on Devices (FedPoD) FedPoD enables devices to obtain highly customized models while maintaining communication efficiency.
  arXiv  Detail & Related papers  (2023-05-23T16:59:20Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data [0.0]
  This paper proposes a distributed learning framework that shares local data collected in ten locations in the western USA throughout local agents. The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling.
  arXiv  Detail & Related papers  (2022-09-15T22:34:06Z)
• Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings [44.4879068879732]
  This paper presents a complete pipeline for resolving ambiguities during the data association. Its core is a robust self-tuning data association that adapts the search area depending on the entropy of the measurements. We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany.
  arXiv  Detail & Related papers  (2022-07-28T12:29:39Z)
• Jalisco's multiclass land cover analysis and classification using a novel lightweight convnet with real-world multispectral and relief data [51.715517570634994]
  We present our novel lightweight (only 89k parameters) Convolution Neural Network (ConvNet) to make LC classification and analysis. In this work, we combine three real-world open data sources to obtain 13 channels. Our embedded analysis anticipates the limited performance in some classes and gives us the opportunity to group the most similar.
  arXiv  Detail & Related papers  (2022-01-26T14:58:51Z)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)
• Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances [44.28969320556008]
  Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. In this paper, we study a variety of machine learning (ML) approaches for SSF over the US mainland.
  arXiv  Detail & Related papers  (2020-06-14T18:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.