Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

Wildfires play a crucial role in the transformation of forest ecosystems and exert a significant influence on the global climate over geological timescales. Recent shifts in climate patterns and intensified human–forest interactions have led to an increase in the incidence of wildfires. These fires are characterized by their extensive coverage, higher frequency, and prolonged duration, rendering them increasingly destructive. To mitigate the impact of wildfires on climate change, ecosystems, and biodiversity, it is imperative to conduct systematic monitoring of wildfire progression and evaluate their environmental repercussions on a global scale. Satellite remote sensing is a powerful tool, offering precise and timely data on terrestrial changes, and has been extensively utilized for wildfire identification, tracking, and impact assessment at both local and regional levels. The Canada Centre for Mapping and Earth Observation, in collaboration with the Canadian Forest Service, has developed a comprehensive National Burned Area Composite (NBAC). This composite serves as a benchmark for curating a bi-temporal multi-source satellite image dataset for change detection, compiled from the archives of Sentinel-2, Sentinel-1, and ALOS-2 PALSAR-2. To our knowledge, this dataset is the inaugural large-scale, multi-source, and multi-frequency satellite image dataset with 20 m spatial resolution for wildfire mapping, monitoring, and evaluation. It harbors significant potential for enhancing wildfire management strategies, building upon the profound advancements in deep learning that have contributed to the field of remote sensing. Based on our curated dataset, which encompasses major wildfire events in Canada, we conducted a systematic evaluation of the capability of multi-source satellite earth observation data in identifying wildfire-burned areas using statistical analysis and deep learning. Our analysis compares the difference between burned and unburned areas using post-event observation solely or bi-temporal (pre- and post-event) observations across diverse land cover types. We demonstrate that optical satellite data yield higher separability than C-Band and L-Band Synthetic Aperture Radar (SAR), which exhibit considerable overlap in burned and unburned sample distribution, as evidenced by SAR-based boxplots. With U-Net, we further explore how different input channels influence the detection accuracy. Our findings reveal that deep neural networks enhance SAR’s performance in mapping burned areas. Notably, C-Band SAR shows a higher dependency on pre-event data than L-Band SAR for effective detection. A comparative analysis of U-Net and its variants indicates that U-Net works best with single-sensor data, while the late fusion architecture marginally surpasses others in the fusion of optical and SAR data. Accuracy across sensors is highest in closed forests, with sequentially lower performance in open forests, shrubs, and grasslands. Future work will extend the data from both spatial and temporal dimensions to encompass varied vegetation types and climate zones, furthering our understanding of multi-source and multi-frequency satellite remote sensing capabilities in wildfire detection and monitoring.

Active fire detection is essential for early warning of wildfires to help suppress and mitigate damage. This study presents an AF-Net model based on object-contextual representations (OCR) for active fire segmentation from very high-resolution (VHR) unmanned aerial vehicles (UAVs) remote sensing images. To efficiently detect heat anomalies in forests from large UAV scenes, we have to handle the class imbalance between small active fire pixels and large-area complex background information. Class imbalance affects the model optimization and makes the training process stuck at a local minimum. Our work aims to address this issue by improving the object-contextual feature representations associated with fire in three ways. First, we employ a grid-based sampling strategy by constraining sampling ranges and reducing background samples. It improves the proportion of foreground pixels from 5.6% to 7.9% and maintains at least one active fire pixel in each sample. Then, we simplify the OCR module to strengthen small object representations related to the active fire using a self-attention unit. The OCR module receives multi-scale pixel representations as input from the HRNet-W48 backbone. Lastly, the weighted binary cross-entropy loss and the Lovász hinge loss are combined to improve the detection accuracy by optimizing the foreground IoU. We evaluate the performance of the proposed AF-Net on one aerial active fire benchmark (FLAME dataset). The proposed framework improves the mIoU score from 78.17% (baseline UNet) to 91.14%.

Deep Neural Networks (DNNs) have attained remarkable success in aerial detection tasks, yet they remain susceptible to adversarial samples, posing a significant challenge for their practical applications. While numerous transferable attacks have been proposed, they frequently overlook the essential balance between attack effectiveness and the feasibility of physical implementation. In this paper, we concentrate our efforts on adversarial attacks against aerial detection, crafting transferable adversarial patches that can be implemented in the physical world. To this end, we introduce localized pattern corruptions, such as light spots and shadows, around the target during the training phase. These corruptions could pull the image distributions closer to the decision boundaries of the surrogate model, thereby enhancing the transferability of patches. In addition, we avoid directly optimizing adversarial patterns with traditional gradient-based techniques. Instead, we opt to update the weights of a specialized generator, which employs MLPs as its core component for mapping purposes. To verify the effectiveness of our method, we conduct experiments across both digital and physical domains. The results reveal that our approach outperforms state-of-the-art methods in terms of attack performance. Our code is available at https://github.com/zhangyu13a/CPA .