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In the context of global climate change and rising anthropogenic loads, outbreaks of both endemic and invasive pests, pathogens, and diseases pose an increasing threat to the health, resilience, and productivity of natural forests and forest plantations worldwide. The effective management of such threats depends on the opportunity for early-stage action helping to limit the damage expand, which is difficult to implement for large territories. Recognition technologies based on the analysis of Earth observation data are the basis for effective tools for monitoring the spread of degradation processes, supporting pest population control, forest management, and conservation strategies in general. In this study, we present a machine learning-based approach for recognizing damaged forests using open source remote sensing images of Sentinel-2 supported with Google Earth data on the example of bark beetle, Polygraphus proximus Blandford, polygraph. For the algorithm development, we first investigated and annotated images in channels corresponding to natural color perception--red, green, and blue--available at Google Earth. Deep neural networks were applied in two problem formulations: semantic segmentation and detection. As a result of conducted experiments, we developed a model that is effective for a quantitative assessment of the changes in target objects with high accuracy, achieving 84.56% of F1-score, determining the number of damaged trees and estimating the areas occupied by withered stands. The obtained damage masks were further integrated with medium-resolution Sentinel-2 images and achieved 81.26% of accuracy, which opened the opportunity for operational monitoring systems to recognize damaged forests in the region, making the solution both rapid and cost-effective. Additionally, a unique annotated dataset has been collected to recognize forest areas damaged by the polygraph in the region of study. [ABSTRACT FROM AUTHOR]

Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct a rapid and accurate assessment of the damage and take measures to restore critical infrastructure as quickly as possible. Remote sensing monitoring using artificial intelligence is a promising tool for estimating the extent of flooded areas. However, monitoring flood events still presents some challenges due to varying weather conditions and cloud cover that can limit the use of visible satellite data. Additionally, satellite observations may not always correspond to the flood peak, and it is essential to estimate both the extent and volume of the flood. To address these challenges, we propose a methodology that combines multispectral and radar data and utilizes a deep neural network pipeline to analyze the available remote sensing observations for different dates. This approach allows us to estimate the depth of the flood and calculate its volume. Our study uses Sentinel-1, Sentinel-2 data, and Digital Elevation Model (DEM) measurements to provide accurate and reliable flood monitoring results. To validate the developed approach, we consider a flood event occurred in 2021 in Ushmun. As a result, we succeeded to evaluate the volume of that flood event at 0.0087 km3. Overall, our proposed methodology offers a simple yet effective approach to monitoring flood events using satellite data and deep neural networks. It has the potential to improve the accuracy and speed of flood damage assessments, which can aid in the timely response and recovery efforts in affected regions. [ABSTRACT FROM AUTHOR]

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis of the environment. The remote sensing domain provides vast amounts of satellite data, but it also poses challenges associated with processing this data. Baseline solutions with intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, and emergency situation analysis using satellite data. Despite these advances, two major issues with high-performing artificial intelligence algorithms remain in the current decade. The first issue relates to the availability of data. To train a robust algorithm, a reasonable amount of well-annotated training data is required. The second issue is the availability of satellite data, which is another concern. Even though there are a number of data providers, high-resolution and up-to-date imagery is extremely expensive. This paper aims to address these challenges by proposing an effective pipeline for building segmentation that utilizes freely available Sentinel-2 data with 10 m spatial resolution. The approach we use combines a super-resolution (SR) component with a semantic segmentation component. As a result, we simultaneously consider and analyze SR and building segmentation tasks to improve the quality of the infrastructure analysis through medium-resolution satellite data. Additionally, we collected and made available a unique dataset for the Russian Federation covering area of 1091.2 square kilometers. The dataset provides Sentinel-2 imagery adjusted to the spatial resolution of 2.5 m and is accompanied by semantic segmentation masks. The building footprints were created using OpenStreetMap data that was manually checked and verified. Several experiments were conducted for the SR task, using advanced image SR methods such as the diffusion-based SR3 model, RCAN, SRGAN, and MCGR. The MCGR network produced the best result, with a PSNR of 27.54 and SSIM of 0.79. The obtained SR images were then used to tackle the building segmentation task with different neural network models, including DeepLabV3 with different encoders, SWIN, and Twins transformers. The SWIN transformer achieved the best results, with an F1-score of 79.60. [ABSTRACT FROM AUTHOR]