1,298 research outputs found
Applicability of Multispectral Imagery for Detection of Prescribed Fires and Rekindling
Forest fires are an increasingly relevant problem nowadays with the worsening of global warming’s most severe consequences. These fire occurrences, that can cause immense damage to forest ecosystems and have a great negative impact in peoples lives,begin often with rekindles. These problems can be very difficult to tackle, needing to involve a lot of people to surveil the areas at risk. A system capable of executing this surveillance protocol and alerting the fire fighting authorities of fire and possible rekindle occurrences would be extremely beneficial in these scenarios.A system aiming to achieve this goal is being implemented, composed of an UAV equipped with a multispectral camera, capturing aerial images of these areas. This dissertation presents a fire detection model to be used in prescribed fires and rekindling situations, identifying fire instances within the captured images. It makes use of the camera’s various spectral bands to highlight the areas at greatest risk and of deep learning technology to autonomously recognise these areas.IncĂŞndios florestais sĂŁo um problema cada vez mais relevante nos dias de hoje com o agravamento das consequĂŞncias mais graves do aquecimento global. Estas ocorrĂŞncias,que podem causar imensos danos aos ecossistemas florestais e ter um grande impacto negativo na vida das pessoas, sĂŁo muitas vezes iniciadas por reacendimentos. Estes problemas podem ser muito difĂceis de combater, necessitando de envolver muitas pessoas para vigiar as áreas de risco. Um sistema capaz de executar este protocolo de vigilância e alertar as autoridades de combate a incĂŞndio sobre fogos e possĂveis reacendimentos seria extremamente benĂ©fico nestes cenários.Para alcançar este objetivo, está a ser implementado um sistema composto por um UAV, equipado com uma câmera multiespectral, que irá capturar imagens aĂ©reas dessas áreas. Esta dissertação apresenta um modelo de detecção de incĂŞndios para ser utilizado em situações de fogos controlados e reacendimentos, identificando ocorrĂŞncias de fogo nas imagens capturadas. Faz uso das várias bandas espectrais da câmera para destacar as áreas de maior risco e de tecnologia de aprendizagem automática para reconhecer essas áreas de forma autĂ´noma
Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020
The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves
HOP Queue: Hyperspectral Onboard Processing Queue Demonstration
The HOP Queue (Hyperspectral Onboard Processing Queue) demonstration leverages emerging COTS AI accelerators and GPUs to perform on-board processing of hyperspectral imagery data, with the aim of providing near- real time conservation-oriented data and metrics from Low Earth Orbit (LEO). These include forest health, fire detection, and coastal water health. Phase 1 of this project is currently underway, including a completed lab demonstration of this technology and ongoing flight testing. The data from this mission will support Northrop Grumman’s enterprise “Technology for Conservation” campaign and will be provided to NASA’s Surface Biology and Geology (SBG) organization, as well as other conservation efforts
A Deep Learning Framework in Selected Remote Sensing Applications
The main research topic is designing and implementing a deep learning framework applied to remote sensing. Remote sensing techniques and applications play a crucial role in observing the Earth evolution, especially nowadays, where the effects of climate change on our life is more and more evident.
A considerable amount of data are daily acquired all over the Earth. Effective exploitation of this information requires the robustness, velocity and accuracy of deep learning. This emerging need inspired the choice of this topic.
The conducted studies mainly focus on two European Space Agency (ESA) missions: Sentinel 1 and Sentinel 2. Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their open access policy. The increasing interest gained by these satellites in the research laboratory and applicative scenarios pushed us to utilize them in the considered framework. The combined use of Sentinel 1 and Sentinel 2 is crucial and very prominent in different contexts and different kinds of monitoring when the growing (or changing) dynamics are very rapid.
Starting from this general framework, two specific research activities were identified and investigated, leading to the results presented in this dissertation. Both these studies can be placed in the context of data fusion.
The first activity deals with a super-resolution framework to improve Sentinel 2 bands supplied at 20 meters up to 10 meters. Increasing the spatial resolution of these bands is of great interest in many remote sensing applications, particularly in monitoring vegetation, rivers, forests, and so on.
The second topic of the deep learning framework has been applied to the multispectral Normalized Difference Vegetation Index (NDVI) extraction, and the semantic segmentation obtained fusing Sentinel 1 and S2 data. The S1 SAR data is of great importance for the quantity of information extracted in the context of monitoring wetlands, rivers and forests, and many other contexts.
In both cases, the problem was addressed with deep learning techniques, and in both cases, very lean architectures were used, demonstrating that even without the availability of computing power, it is possible to obtain high-level results.
The core of this framework is a Convolutional Neural Network (CNN).
{CNNs have been successfully applied
to many image processing problems,
like super-resolution,
pansharpening,
classification,
and others,
because of several advantages such as
(i) the capability to approximate complex non-linear
functions,
(ii) the ease of training that allows
to avoid time-consuming handcraft filter design,
(iii) the parallel computational architecture.
Even if a large amount of "labelled" data is required for training, the CNN performances pushed me to this architectural choice.} In our S1 and S2 integration task, we have faced and overcome the problem of manually labelled data with an approach based on integrating these two different sensors.
Therefore, apart from the investigation in Sentinel-1 and Sentinel-2 integration, the main contribution in both cases of these works is, in particular, the possibility of designing a CNN-based solution that can be distinguished by its lightness from a computational point of view and consequent substantial saving of time compared to more complex deep learning state-of-the-art solutions
Supervised Machine Learning Approaches on Multispectral Remote Sensing Data for a Combined Detection of Fire and Burned Area
Bushfires pose a severe risk, among others, to humans, wildlife, and infrastructures. Rapid detection of fires is crucial for fire-extinguishing activities and rescue missions. Besides, mapping burned areas also supports evacuation and accessibility to emergency facilities. In this study, we propose a generic approach for detecting fires and burned areas based on machine learning (ML) approaches and remote sensing data. While most studies investigated either the detection of fires or mapping burned areas, we addressed and evaluated, in particular, the combined detection on three selected case study regions. Multispectral Sentinel-2 images represent the input data for the supervised ML models. First, we generated the reference data for the three target classes, burned, unburned, and fire, since no reference data were available. Second, the three regional fire datasets were preprocessed and divided into training, validation, and test subsets according to a defined schema. Furthermore, an undersampling approach ensured the balancing of the datasets. Third, seven selected supervised classification approaches were used and evaluated, including tree-based models, a self-organizing map, an artificial neural network, and a one-dimensional convolutional neural network (1D-CNN). All selected ML approaches achieved satisfying classification results. Moreover, they performed a highly accurate fire detection, while separating burned and unburned areas was slightly more challenging. The 1D-CNN and extremely randomized tree were the best-performing models with an overall accuracy score of 98 % on the test subsets. Even on an unknown test dataset, the 1D-CNN achieved high classification accuracies. This generalization is even more valuable for any use-case scenario, including the organization of fire-fighting activities or civil protection. The proposed combined detection could be extended and enhanced with crowdsourced data in further studies
Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms
ProducciĂłn CientĂficaPrescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de UrĂa” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.Ministerio de EconomĂa, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AGL2017-86075-C2-1-R)Junta de Castilla y LeĂłn (project LE001P17
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