FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting

• URL: http://arxiv.org/abs/2512.03369v1
• Date: Wed, 03 Dec 2025 02:02:47 GMT
• Title: FireSentry: A Multi-Modal Spatio-temporal Benchmark Dataset for Fine-Grained Wildfire Spread Forecasting
• Authors: Nan Zhou, Huandong Wang, Jiahao Li, Han Li, Yali Song, Qiuhua Wang, Yong Li, Xinlei Chen,
• Abstract summary: We present FireSentry, a provincial-scale multi-modal wildfire dataset characterized by sub-meter spatial and sub-second temporal resolution.<n>FireSentry provides visible and infrared video streams, in-situ environmental measurements, and manually validated fire masks.<n>Building on FireSentry, we establish a comprehensive benchmark encompassing physics-based, data-driven, and generative models.
• Score: 41.82363110982653
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-grained wildfire spread prediction is crucial for enhancing emergency response efficacy and decision-making precision. However, existing research predominantly focuses on coarse spatiotemporal scales and relies on low-resolution satellite data, capturing only macroscopic fire states while fundamentally constraining high-precision localized fire dynamics modeling capabilities. To bridge this gap, we present FireSentry, a provincial-scale multi-modal wildfire dataset characterized by sub-meter spatial and sub-second temporal resolution. Collected using synchronized UAV platforms, FireSentry provides visible and infrared video streams, in-situ environmental measurements, and manually validated fire masks. Building on FireSentry, we establish a comprehensive benchmark encompassing physics-based, data-driven, and generative models, revealing the limitations of existing mask-only approaches. Our analysis proposes FiReDiff, a novel dual-modality paradigm that first predicts future video sequences in the infrared modality, and then precisely segments fire masks in the mask modality based on the generated dynamics. FiReDiff achieves state-of-the-art performance, with video quality gains of 39.2% in PSNR, 36.1% in SSIM, 50.0% in LPIPS, 29.4% in FVD, and mask accuracy gains of 3.3% in AUPRC, 59.1% in F1 score, 42.9% in IoU, and 62.5% in MSE when applied to generative models. The FireSentry benchmark dataset and FiReDiff paradigm collectively advance fine-grained wildfire forecasting and dynamic disaster simulation. The processed benchmark dataset is publicly available at: https://github.com/Munan222/FireSentry-Benchmark-Dataset.

Related papers

• U-Net with Hadamard Transform and DCT Latent Spaces for Next-day Wildfire Spread Prediction [0.6999740786886536]
  We develop a lightweight tool for next-day wildfire spread prediction using multimodal satellite data as input.<n>Deep learning model, which we call Transform Domain Fusion UNet, incorporates trainable Hadamard Transform and Discrete Cosine Transform layers.<n>We show that the proposed latent space fusion model balances accuracy and efficiency under a lightweight setting.
  arXiv  Detail & Related papers  (2026-02-12T07:45:53Z)
• Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking [51.56484100374058]
  We evaluate whether a fully automatic, purely feedback-driven ESN can serve as a competitive alternative to widely used statistical forecasting methods.<n>Forecast accuracy is measured using MASE and sMAPE and benchmarked against simple benchmarks like drift and seasonal naive and statistical models.
  arXiv  Detail & Related papers  (2026-02-03T16:01:22Z)
• PyroFocus: A Deep Learning Approach to Real-Time Wildfire Detection in Multispectral Remote Sensing Imagery [0.0]
  Rapid and accurate wildfire detection is crucial for emergency response and environmental management.<n>In airborne and spaceborne missions, real-time algorithms must distinguish between no fire, active fire, and post-fire conditions.<n>We introduce PyroFocus, a two-stage pipeline that performs fire classification followed by fire radiative power (FRP) regression or segmentation to reduce inference time and computational cost for onboard deployment.
  arXiv  Detail & Related papers  (2025-12-02T21:59:45Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection [4.796400107449626]
  We propose a framework for real-time wildfire monitoring and fire source detection on UAV platforms.<n>Specifically, in Stage 1, we utilize a policy network to identify and discard redundant video clips.<n>In Stage 2, once the frame is classified as "fire," we employ the improved YOLOv8 model to localize the fire source.
  arXiv  Detail & Related papers  (2025-08-22T18:27:31Z)
• Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model [55.25659103706409]
  This framework achieves state-of-the-art performance for our designed foundation model, YingLong.<n>YingLong is a non-causal, bidirectional attention encoder-only transformer trained through masked token recovery.<n>We release four foundation models ranging from 6M to 300M parameters, demonstrating superior results in zero-shot tasks.
  arXiv  Detail & Related papers  (2025-05-20T14:31:06Z)
• Detecting Wildfire Flame and Smoke through Edge Computing using Transfer Learning Enhanced Deep Learning Models [7.222438368943405]
  This study underscores Transfer Learning's (TL) significance in boosting the performance of object detectors for identifying wildfire smoke and flames.<n>We explore a two-stage cascaded TL method, utilizing D-Fire or FASDD as initial stage target datasets and AFSE as the subsequent stage.<n>Through fine-tuning, TL significantly enhances detection precision, achieving up to 79.2% mean Average Precision (mAP@0.5), reduces training time, and increases model generalizability.
  arXiv  Detail & Related papers  (2025-01-15T08:04:44Z)
• Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data [15.516417504988313]
  We develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN)<n>FIDN uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire.<n>It shows higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches.
  arXiv  Detail & Related papers  (2024-12-02T11:35:31Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• Obscured Wildfire Flame Detection By Temporal Analysis of Smoke Patterns Captured by Unmanned Aerial Systems [0.799536002595393]
  This research paper addresses the challenge of detecting obscured wildfires in real-time using drones equipped only with RGB cameras. We propose a novel methodology that employs semantic segmentation based on the temporal analysis of smoke patterns in video sequences.
  arXiv  Detail & Related papers  (2023-06-30T19:45:43Z)
• A Meta-Learning Approach to Predicting Performance and Data Requirements [163.4412093478316]
  We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset. We introduce a novel piecewise power law (PPL) that handles the two data differently.
  arXiv  Detail & Related papers  (2023-03-02T21:48:22Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)

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.