Predicting wildfire spread behavior is an extremely important task for many countries. On a small scale, it possible to ensure constant monitoring of the natural landscape through ground means. However, on scale large countries, this becomes practically impossible due remote and vast forest territories. The most promising source data in case that can provide global sensing data. Currently, main challenge development effective pipeline combines geospatial collection application advanced...

Remote sensing of forests is a powerful tool for monitoring the biodiversity ecosystems, maintaining general planning, and accounting resources. Various sensors bring together heterogeneous data, advanced machine learning methods enable their automatic handling in wide territories. Key forest properties usually under consideration environmental studies include dominant species, tree age, height, basal area timber stock. Being proxies stand productivity, they can be utilized carbon stock...

The canopy height model (CHM) is a representation of the top vegetation from surrounding ground level. It crucial for extraction various forest characteristics, instance, timber stock estimations and growth measurements. There are different ways obtaining height, such as through ground-based observations or interpretation remote sensing images. severe downside field measurement its cost acquisition difficulty. Therefore, utilizing data is, in many cases, preferable. enormous advances...

Satellite data allows us to solve a wide range of challenging tasks remotely, including monitoring changing environmental conditions, assessing resources, and evaluating hazards. Computer vision algorithms such as convolutional neural networks have proven be powerful tools for handling huge visual datasets. Although the number satellite imagery is constantly growing artificial intelligence advancing, present sticking point in remote sensing studies quality amount annotated Typically, manual...

Among different forest inventory problems, one of the most basic is defining dominant species. These data are crucial in management to determine category, and a cheaper remote sensing-based approach would be useful supplement field surveys. We used WorldView multispectral satellite imagery address this problem as an image segmentation task dividing into regions with particular Neural networks have recently become tools for kind problem, including incomplete or erroneous training labels....

Estimation of terrestrial carbon balance is one the key tasks in understanding and prognosis climate change impacts development tools policies according to mitigation adaptation strategies. Forest ecosystems are major pools stocks affected by controversial processes influencing stability. Therefore, monitoring forest a proper inventory management resources planning their sustainable use. In this survey, we discuss which computer vision techniques applicable most important aspects actions,...

Wildfires play a pivotal role in environmental processes and the sustainable development of ecosystems. Timely responses can significantly reduce damages consequences caused by their spread. Several critical issues wildfire behavior analysis include fire occurrence forecasting, early detection, spread prediction. In this study, we focus on which is valuable tool for facilitating earlier intervention. Conventional approaches primarily rely computation indices based weather conditions....

Information on forest composition, specifically tree types and their distribution, aids in timber stock calculation can help to better understand the biodiversity a particular region. Automatic satellite imagery analysis significantly accelerate process of type classification, which is traditionally carried out by ground-based observation. Although computer vision methods have proven efficiency remote sensing tasks, specific challenges arise forestry applications. The inventory data often...

Deep convolutional neural networks are highly efficient for computer vision tasks using plenty of training data. However, there remains a problem small datasets. For addressing this the pipeline which handles rare object types and an overall lack data to build well-performing models that provide stable predictions is required. This article reports on comprehensive framework <i>XtremeAugment</i> provides easy, reliable, scalable way collect image datasets efficiently label augment collected...

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis the environment. The remote sensing domain provides vast amounts satellite data, but it also poses challenges associated with processing this data. Baseline solutions intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, emergency situation Despite these advances, two major issues high-performing...

In the context of global climate change and rising anthropogenic loads, outbreaks both endemic invasive pests, pathogens, diseases pose an increasing threat to health, resilience, productivity natural forests forest plantations worldwide. The effective management such threats depends on opportunity for early-stage action helping limit damage expand, which is difficult implement large territories. Recognition technologies based analysis Earth observation data are basis tools monitoring spread...

Usage of multispectral satellite imaging data opens vast possibilities for monitoring and quantitatively assessing properties or objects interest on a global scale. Machine learning computer vision (CV) approaches show themselves as promising tools automatizing image analysis. However, there are limitations in using CV data. Mainly, the crucial one is amount available model training. This paper presents novel augmentation approach called MixChannel that helps to address this limitation...

Remote sensing tasks play a very important role in the domain of and measuring, can be specific. Advances computer vision techniques allow for extraction various information from remote satellite imagery. This is crucial making quantitative qualitative assessments monitoring forest clearing protected areas power lines, as well environmental analysis, particular carbon footprint, which highly relevant task. Solving these problems requires precise segmentation mask. Although mask data has been...

Large datasets catalyze the rapid expansion of deep learning and computer vision. At same time, in many domains, there is a lack training data, which may become an obstacle for practical application vision models. To overcome this problem, it popular to apply image augmentation. When dataset contains instance segmentation masks, possible instance-level It operates by cutting from original pasting new backgrounds. This article challenges with objects present various domains. We introduce...

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 rapid accurate assessment damage take measures restore critical infrastructure as quickly possible. Remote sensing monitoring using artificial intelligence promising tool for estimating extent flooded areas. However, flood still presents some challenges due varying weather conditions cloud cover limit use visible satellite data....

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite usefulness NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make possible generate artificial information, example, solve colorization problem. In this research, we aim investigate whether approach can produce only...

Today deep convolutional neural networks (CNNs) push the limits for most computer vision problems, define trends, and set state-of-the-art results. In remote sensing tasks such as object detection semantic segmentation, CNNs reach SotA performance. However, precise performance, require much high-quality training data. Rare objects variability of environmental conditions strongly affect prediction stability accuracy. To overcome these data restrictions, it is common to consider various...

Chl-a concentration is one of the key characteristics marine areas related to photosynthesis, along with oxygen levels and water salinity. Most studies focus on estimating chl-a in closed bodies, rivers, coastal tropical temperate Earth belts are therefore limited specific regions also require direct measurements chemical analysis obtain precise information about environmental conditions. Remote sensing techniques spatial modeling aim offer tools for rapid global climate ecological changes....

Plant segmentation is a challenging computer vision task due to plant images complexity. For many practical problems, we have solve even more difficult tasks. We need distinguish parts rather than the whole plant. The major complication of multi-part absence well-annotated datasets. It very time-consuming and expensive annotate datasets manually on object level. In this article, propose use weakly supervised learning for pseudo-annotation. goal train part model using only bounding boxes...

Increasingly, automation helps to minimize human involvement in many mundane aspects of life, especially retail. During the pandemic it became clear that shop not only reduce labor and speedup service but also spread disease. The recognition produce has no barcode remains among processes are complicated automate. ability distinguish weighted goods is necessary correctly bill a customer at self checkout station. A computer vision system can be deployed on either smart scales or cash...

Tree age is one of the key characteristics a forest, along with tree species and height. It affects management decisions forest owners allows researchers to analyze environmental in support sustainable development. Although primary significance, it can be unknown for remote areas large territories. Currently, sensing (RS) data supports rapid information gathering wide To automate RS processing estimate characteristics, machine learning (ML) approaches are applied. there different sources...

Currently, remote sensing techniques assist in various environmental applications and facilitate observation spatial analysis. Machine learning algorithms allow researchers to find dependencies satellite data vegetation cover properties. One of the significant tasks for ecological assessment is associated with estimating forest characteristics monitoring changes over time. In contrast general computer vision domain, forestry measurements have their own specific requirements necessitate...