1,720,970 research outputs found
Data science for geo-referenced and heterogeneous data analysis with applications in the emergency management domain
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Sentinel-1 Flood Delineation with Supervised Machine Learning
Full text linkFloods are one of the major natural hazards in terms of affected people and economic damages. The increasing and often uncontrolled urban sprawl together with climate change effects will make future floods more frequent and impacting. An accurate flood mapping is of paramount importance in order to update hazard and risk maps and to plan prevention measures. In this paper, we propose the use of a supervised machine learning approach for flood delineation from satellite data. We train and evaluate the proposed algorithm using Sentinel-1 acquisition and certified flood delineation maps produced by the Copernicus Emergency Management Service across different geographical regions in Europe, achieving increased performances against previously proposed supervised machine learning approaches for flood mapping
Detection of GNSS Ionospheric Scintillations based on Machine Learning Decision Tree
Full text linkThis paper proposes a methodology for automatic, accurate and early detection of amplitude ionospheric scintillation events, based on machine learning algorithms, applied on big sets of 50 Hz post-correlation data provided by a GNSS receiver. Experimental results on real data show that this approach can considerably improve traditional methods, reaching a detection accuracy of 98%, very close to human-driven manual classification. Moreover, the detection responsiveness is enhanced, enabling early scintillation alerts
Supervised Burned Areas delineation by means of Sentinel-2 imagery and Convolutional Neural Networks
Full text linkWildfire events are increasingly threatening our lands, cities, and lives. To contrast this phenomenon and to limit its damages, governments around the globe are trying to find proper counter-measures, identifying prevention and monitoring as two key factors to reduce wildfires impact worldwide. In this work, we propose two deep convolutional neural networks to automatically detect and delineate burned areas from satellite acquisitions, assessing their performances at scale using validated maps of burned areas of historical wildfires. We demonstrate that the proposed networks substantially improve the burned area delineation accuracy over conventional methods
A machine learning approach to GNSS scintillation detection: automatic soft inspection of the events
No full textClassical approaches for the automatic detection of ionospheric scintillation events in Global Navigation Satellite System (GNSS) receivers are based on the observation of indices (e.g. S4) that are obtained by processing parameters assessed at the signal processing stages of the receiver. Such values are the result of algorithms that imply specific processing choices (such as detrending, averaging and threshold operations) which influence the final performance of the detection. To reach good levels of accuracy and generalization for the identification and classification of the physical phenomenon, these approaches may require an additional human effort to refine the detection results by means of a manual inspection of the events, which is expensive and time consuming. This paper proposes a new methodology for the detection of ionospheric scintillation events based on Machine Learning techniques applied to GNSS data. This method, based on Decision Trees algorithms, aims at overcoming the limitation of the classical approaches by identifying scintillation events “as if” done by a human operator through visual inspection. This approach is automatic, unbound from traditional scintillation indices and features improved detection, false alarm, and missed detection rates when compared to standard methods
Double-Step deep learning framework to improve wildfire severity classification
Full text linkWildfires are dangerous events which cause huge losses under natural, humanitarian and economical perspectives. To contrast their impact, a fast and accurate restoration can be improved through the automatic census of the event in terms of (i) delin- eation of the affected areas and (ii) estimation of damage severity, using satellite images. This work proposes to extend the state- of-the-art approach, named Double-Step U-Net (DS-UNet), able to automatically detect wildfires in satellite acquisitions and to associate a damage index from a defined scale. As a deep learning network, the DS-UNet model performance is strongly dependent on many factors. We propose to focus on alternatives in its main architecture by designing a configurable Double-Step Framework, which allows inspecting the prediction quality with different loss-functions and convolutional neural networks used as backbones. Experimental results show that the proposed framework yields better performance with up to 6.1% lower RMSE than current state of the art
A Dataset for Burned Area Delineation and Severity Estimation from Satellite Imagery
Full text linkThe ability to correctly identify areas damaged by forest wildfires is essential to plan and monitor the restoration process and estimate the environmental damages after such catastrophic events. The wide availability of satellite data, combined with the recent development of machine learning and deep learning methodologies applied to the computer vision field, makes it extremely interesting to apply the aforementioned techniques to the field of automatic burned area detection. One of the main issues in such a context is the limited amount of labeled data, especially in the context of semantic segmentation. In this paper, we introduce a publicly available dataset for the burned area detection problem for semantic segmentation. The dataset contains 73 satellite images of different forests damaged by wildfires across Europe with a resolution of up to 10m per pixel. Data were collected from the Sentinel-2 L2A satellite mission and the target labels were generated from the Copernicus Emergency Management Service (EMS) annotations, with five different severity levels, ranging from undamaged to completely destroyed. Finally, we report the benchmark values obtained by applying a Convolutional Neural Network on the proposed dataset to address the burned area identification problem
Deep learning models for road passability detection during flood events using social media data
Full text linkDuring natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked / open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks at the same time. Both problems are treated as a single class classification problem by the usage of a compactness loss. The study is performed on a set of tweets, posted during flooding events, that contain (i)~metadata and (ii)~visual information. We study the usefulness of each source of data and the combination of both. Finally, we do a study of the performance gain from ensembling different networks. Through the experimental results we prove that the proposed double-ended NN makes the model almost two times faster and memory lighter while improving the results with respect to training two separate networks to solve each problem independently
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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