171 research outputs found
Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model
Landslide susceptibility prediction has always been an important and
challenging content. However, there are some uncertain problems to be solved in
susceptibility modeling, such as the error of landslide samples and the complex
nonlinear relationship between environmental factors. A self-screening graph
convolutional network and long short-term memory network (SGCN-LSTM) is
proposed int this paper to overcome the above problems in landslide
susceptibility prediction. The SGCN-LSTM model has the advantages of wide width
and good learning ability. The landslide samples with large errors outside the
set threshold interval are eliminated by self-screening network, and the
nonlinear relationship between environmental factors can be extracted from both
spatial nodes and time series, so as to better simulate the nonlinear
relationship between environmental factors. The SGCN-LSTM model was applied to
landslide susceptibility prediction in Anyuan County, Jiangxi Province, China,
and compared with Cascade-parallel Long Short-Term Memory and Conditional
Random Fields (CPLSTM-CRF), Random Forest (RF), Support Vector Machine (SVM),
Stochastic Gradient Descent (SGD) and Logistic Regression (LR) models.The
landslide prediction experiment in Anyuan County showed that the total accuracy
and AUC of SGCN-LSTM model were the highest among the six models, and the total
accuracy reached 92.38 %, which was 5.88%, 12.44%, 19.65%, 19.92% and 20.34%
higher than those of CPLSTM-CRF, RF, SVM, SGD and LR models, respectively. The
AUC value reached 0.9782, which was 0.0305,0.0532,0.1875,0.1909 and 0.1829
higher than the other five models, respectively. In conclusion, compared with
some existing traditional machine learning, the SGCN-LSTM model proposed in
this paper has higher landslide prediction accuracy and better robustness, and
has a good application prospect in the LSP field
Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models
Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locationsâPhu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models
Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR) remote-sensing data in the case of scarce GT. The main idea is to combine a specific type of deep convolutional neural networks (CNNs), namely fully convolutional networks (FCNs), with probabilistic graphical models (PGMs). Our method takes advantage of the intrinsic multiscale behavior of FCNs to deal with multiscale data representations and to connect them to a hierarchical Markov model (e.g., making use of a quadtree). As a consequence, the spatial information present in the data is better exploited, allowing a reduced sensitivity to GT incompleteness to be obtained. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. To assess the capabilities of the proposed method, the experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge datasets on the cities of Vaihingen and Potsdam, with some modifications to simulate the spatially sparse GTs that are common in real remote-sensing applications. The results are quite significant, as the proposed approach exhibits a higher producer accuracy than the standard FCNs considered and especially mitigates the impact of scarce GTs on minority classes and small spatial details
Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate
A Review on Deep Learning in UAV Remote Sensing
Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images,
time-series, natural language, audio, video, and many others. In the remote
sensing field, surveys and literature revisions specifically involving DNNs
algorithms' applications have been conducted in an attempt to summarize the
amount of information produced in its subfields. Recently, Unmanned Aerial
Vehicles (UAV) based applications have dominated aerial sensing research.
However, a literature revision that combines both "deep learning" and "UAV
remote sensing" thematics has not yet been conducted. The motivation for our
work was to present a comprehensive review of the fundamentals of Deep Learning
(DL) applied in UAV-based imagery. We focused mainly on describing
classification and regression techniques used in recent applications with
UAV-acquired data. For that, a total of 232 papers published in international
scientific journal databases was examined. We gathered the published material
and evaluated their characteristics regarding application, sensor, and
technique used. We relate how DL presents promising results and has the
potential for processing tasks associated with UAV-based image data. Lastly, we
project future perspectives, commentating on prominent DL paths to be explored
in the UAV remote sensing field. Our revision consists of a friendly-approach
to introduce, commentate, and summarize the state-of-the-art in UAV-based image
applications with DNNs algorithms in diverse subfields of remote sensing,
grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure
Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping
This paper presents a comprehensive study focusing on the influence of DEM
type and spatial resolution on the accuracy of flood inundation prediction. The
research employs a state-of-the-art deep learning method using a 1D
convolutional neural network (CNN). The CNN-based method employs training input
data in the form of synthetic hydrographs, along with target data represented
by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The
performance of the trained CNN models is then evaluated and compared with the
observed flood event. This study examines the use of digital surface models
(DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM,
with resolutions ranging from 15 to 30 meters. The proposed methodology is
implemented and evaluated in a well-established benchmark location in Carlisle,
UK. The paper also discusses the applicability of the methodology to address
the challenges encountered in a data-scarce flood-prone region, exemplified by
Pakistan. The study found that DTM performs better than DSM at lower
resolutions. Using a 30m DTM improved flood depth prediction accuracy by about
21% during the peak stage. Increasing the resolution to 15m increased RMSE and
overlap index by at least 50% and 20% across all flood phases. The study
demonstrates that while coarser resolution may impact the accuracy of the CNN
model, it remains a viable option for rapid flood prediction compared to
hydrodynamic modeling approaches
Semantic Segmentation of Remote Sensing Images through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models
International audienceDeep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR) remote-sensing data in the case of scarce GT. The main idea is to combine a specific type of deep convolutional neural networks (CNNs), namely fully convolutional networks (FCNs), with probabilistic graphical models (PGMs). Our method takes advantage of the intrinsic multiscale behavior of FCNs to deal with multiscale data representations and to connect them to a hierarchical Markov model (e.g., making use of a quadtree). As a consequence, the spatial information present in the data is better exploited, allowing a reduced sensitivity to GT incompleteness to be obtained. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. To assess the capabilities of the proposed method, the experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge datasets on the cities of Vaihingen and Potsdam, with some modifications to simulate the spatially sparse GTs that are common in real remote-sensing applications. The results are quite significant, as the proposed approach exhibits a higher producer accuracy than the standard FCNs considered and especially mitigates the impact of scarce GTs on minority classes and small spatial details
Propose effective routing method for mobile sink in wireless sensor network
Wireless sensor network is one of the popular technologies used for maximizing the lifetime of network and to enhance the data collection process and energy efficiency by mobility. So, this work was proposed and focused on sink mobility which plays a key role in data collection process. The main challenge task was to discover the route in the active network. We have proposed an opportunistic algorithm in this paper with mobile sink to discover the ideal path starting the source to destination node. The proposed system has focused on a sensor field to sense and to report on building during fires where the sensors could be destroyed. The proposed system was evaluated through simulation and compared with existing algorithms (Genetic algorithm, multi-layer perceptron neural network). The performance which showed data delivery can be increased by up to 95%
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