LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (T_tx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa’s Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting T_tx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces T_tx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above −5 dB in line-of-sight communication scenarios.

Unmanned Aerial Vehicles (UAVs) have always been faced with power management challenges to extend their flight time. Managing power consumption becomes critical, especially in surveillance applications, where the longer flight time results in wider coverage and a cheaper solution. Most of the current studies show new methods for event detection without considering power consumption. This article presents an event-driven four-stage video surveillance pipeline with an efficient video transmission algorithm balancing power consumption and image quality. The surveillance starts automatically when the low-power AI-based onboard processor detects the desired event. When The edge node detects the defined event, a sample image is sent to the server for validation. After validation, a colored image accompanied by N grayscale images are sent to the server. The server colorizes the grayscale images using a convolutional neural network trained by the colored images. In this work, an application of wildfire detection and surveillance has been implemented to show the proof of concept of the TinySurveillance method. The results show that the power consumption of the onboard processing unit in detection mode reduces by at least 4 times; during the surveillance mode, the data transmission rate can be decreased by almost 66% while achieving a competent image quality PSNR_Avg of 41.35 dB, PSNR of 30.94 dB, and output frame rate of 5.2.

The development of high-fidelity wildfire spread models is contingent upon the availability of spatially and temporally aligned multi-layer datasets. Existing global and continental databases have successfully catalogued fire events; however, they are often constrained by coarse spatial resolutions or absence of environmental variables. Additionally, there is a significant scarcity of machine-learning-ready open-access datasets dedicated to the Canadian landscape. This leaves a critical gap in the data available for modelling fire-front dynamics characteristic of Canadian ecosystems. In this paper, we introduce a comprehensive, multi-layer wildfire perimeter prediction dataset engineered specifically for the Canadian boreal forest, covering the period from 2001 to 2020. Unlike previous catalogues, this dataset includes 2,565 distinct fire events with a minimum area threshold of 1 ha (0.01 km2), capturing a more inclusive historical record of fire activity. The curation process involves a rigorous integration of final burned geometries from the Canadian National Fire Database (CNFDB) with fire-adapted environmental covariates, including high-frequency meteorological indices, static topographical features, and fuel parameters. To facilitate machine learning applications, the data is processed into a format suitable for tasks such as fire segmentation and perimeter prediction, with spatial resolutions adapted to the scale of individual fire events. By providing granular inputs for both small-scale ignitions and complex fire perimeters, this dataset serves as a foundational resource for advancing predictive modelling and real-time surveillance pipelines in northern forest environments.

Effective wildfire management demands seamless integration of real-time detection and long-term spread forecasting. This paper proposes a novel power-efficient UAV–satellite hybrid pipeline that synergizes the agility of UAVs with the scale of satellite intelligence. The system begins with a dashboard-guided, multi-UAV detection module that scores fire likelihood from historical satellite data and enables scalable, energy-efficient deployment with low-latency onboard processing. This aerial component ensures persistent surveillance and reliable ignition detection, supported by a Dual LoRa (Long Range) communication scheme for robust and low-power connectivity. It achieves an F1-score of 97.4% while minimizing power consumption to extend operational flight times. Following detection, the pipeline transitions to a dynamic perimeter-prediction phase utilizing a custom Canadian boreal dataset. We employ a Squeeze-and-Excitation Residual U-Net (SE-ResUNet) to model spatiotemporal fire propagation based on static terrain and dynamic environmental features. The model was validated using a dynamic simulation framework that evaluates temporal consistency and convergence behavior against final cumulative burned-area masks, effectively addressing the absence of daily ground truth. Under these conditions, the model achieves a recall of 84% and an AUC of 0.97, demonstrating a strong capability to delineate active fire fronts. By coupling dashboard-driven UAV sensing with satellite-based predictive modeling, this work establishes a modular, foundational framework to support data-scarce forecasting in modern wildfire management.