1,720,971 research outputs found
Spatial data science: from aerial data to discrete spatio-temporal event analysis
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ViGEO: an Assessment of Vision GNNs in Earth Observation
Full text linkSatellite missions and Earth Observation (EO) systems represent fundamental assets for environmental monitoring and the timely identification of catastrophic events, long-term monitoring of both natural resources and human-made assets, such as vegetation, water bodies, forests as well as buildings. Different EO missions enables the collection of information on several spectral bandwidths, such as MODIS, Sentinel-1 and Sentinel-2. Thus, given the recent advances of machine learning, computer vision and the availability of labeled data, researchers demonstrated the feasibility and the precision of land-use monitoring systems and remote sensing image classification through the use of deep neural networks. Such systems may help domain experts and governments in constant environmental monitoring, enabling timely intervention in case of catastrophic events (e.g., forest wildfire in a remote area). Despite the recent advances in the field of computer vision, many works limit their analysis on Convolutional Neural Networks (CNNs) and, more recently, to vision transformers (ViTs). Given the recent successes of Graph Neural Networks (GNNs) on non-graph data, such as time-series and images, we investigate the performances of a recent Vision GNN architecture (ViG) applied to the task of land cover classification. The experimental results show that ViG achieves state-of-the-art performances in multiclass and multilabel classification contexts, surpassing both ViT and ResNet on large-scale benchmarks
Discovering SpatioTemporally Invariant Event Patterns From Mobility Data
Full text linkThe discovery of sequential patterns from spatiotemporal data is known to be a very complex data mining task. The relevance of spatiotemporal patterns to study event correlations in mobility data is established. Prior works addressed either the separate analysis of spatial and temporal dependencies among data, such as the co-location of events, or the study of the joint spatiotemporal properties of the trajectories observed over a region of interest. The aim of this paper is instead to overcome existing approaches by extracting sequences of discrete events showing spatiotemporally invariant properties. For example, if an arbitrary bike sharing station becomes full (all its docks are used) then we will observe an increase in the occupancy level of the bike sharing stations in the surrounding area within ten minutes. We denote such a new pattern as a SpatioTemporally Invariant (STInv) event pattern because we observe several instances in the source data differing just in spatiotemporal shifts. We also propose a new algorithm to mine STInvs based on a prefix-projected sequential pattern growth approach and different quality metrics to quantify the contribution of the spatial invariance. The proposed approach is empirically evaluated on two mobility datasets related to a bike sharing system and traffic data. The results confirm the usability of the proposed solution in real-world scenarios
Density-based Clustering by Means of Bridge Point Identification
Full text linkDensity-based clustering focuses on defining clusters consisting of contiguous regions characterized by similar densities of points. Traditional approaches identify core points first, whereas more recent ones initially identify the cluster borders and then propagate cluster labels within the delimited regions. Both strategies encounter issues in presence of multi-density regions or when clusters are characterized by noisy borders. To overcome the above issues, we present a new clustering algorithm that relies on the concept of bridge point. A bridge point is a point whose neighborhood includes points of different clusters. The key idea is to use bridge points, rather than border points, to partition points into clusters. We have proved that a correct bridge point identification yields a cluster separation consistent with the expectation. To correctly identify bridge points in absence of a priori cluster information we leverage an established unsupervised outlier detection algorithm. Specifically, we empirically show that, in most cases, the detected outliers are actually a superset of the bridge point set. Therefore, to define clusters we spread cluster labels like a wildfire until an outlier, acting as a candidate bridge point, is reached. The proposed algorithm performs statistically better than state-of-the-art methods on a large set of benchmark datasets and is particularly robust to the presence of intra-cluster multiple densities and noisy borders
Vision Transformers for Burned Area Delineation
Full text linkThe automatic identification of burned areas is an important task that was mainly managed manually or semi-automatically in the past. In the last years, thanks to the availability of novel deep neural network architectures, automatic segmentation solutions have been proposed also in the emergency management domain. The most recent works in burned area delineation leverage on Convolutional Neural Networks (CNNs) to automatically identify regions that were previously affected by forest wildfires. A largely adopted segmentation model, U-Net, demonstrated good performances for the task under analysis, but in some cases a high overestimation of burned areas is given, leading to low precision scores. Given the recent advances in the field of NLP and the first successes also in the vision domain, in this paper we investigate the adoption of vision transformers for semantic segmentation to address the burned area identification task. In particular, we explore the SegFormer architecture with two of its variants: the smallest MiT-B0 and the intermediate one MiT-B3. The experimental results show that SegFormer provides better predictions, with higher precision and F1 score, but also better performance in terms of the number of parameters with respect to CNNs
CaBuAr: California burned areas dataset for delineation [Software and Data Sets]
Full text linkForest wildfires represent one of the catastrophic events that, over the last decades, have caused huge environmental and humanitarian damage. In addition to a significant amount of carbon dioxide emission, they are a source of risk to society in both short-term (e.g., temporary city evacuation due to fire) and long-term (e.g., higher risks of landslides) cases. Consequently, the availability of tools to support local authorities in automatically identifying burned areas plays an important role in the continuous monitoring requirement to alleviate the aftereffects of such catastrophic events. The great availability of satellite acquisitions coupled with computer vision techniques represents an important step in developing such tools
Magnifier: A Multigrained Neural Network-Based Architecture for Burned Area Delineation
Full text linkIn crisis management and remote sensing, image segmentation plays a crucial role, enabling tasks like disaster response and emergency planning by analyzing visual data. Neural networks are able to analyze satellite acquisitions and determine which areas were affected by a catastrophic event. The problem in their development in this context is the data scarcity and the lack of extensive benchmark datasets, limiting the capabilities of training large neural network models. In this article, we propose a novel methodology, namely Magnifier, to improve segmentation performance with limited data availability. The Magnifier methodology is applicable to any existing encoder–decoder architecture, as it extends a model by merging information at different contextual levels through a dual-encoder approach: a local and global encoder. Magnifier analyzes the input data twice using the dual-encoder approach. In particular, the local and global encoders extract information from the same input at different granularities. This allows Magnifier to extract more information than the other approaches given the same set of input images. Magnifier improves the quality of the results of +2.65% on average intersection over union while leading to a restrained increase in terms of the number of trainable parameters compared to the original model. We evaluated our proposed approach with state-of-the-art burned area segmentation models, demonstrating, on average, comparable or better performances in less than half of the giga floating point operations per second (GFLOPs)
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
Lucas Anton Colomba
No full textSchulterstück in einem ovalen Medaillon, das von einer Blättergirlande umwunden auf einer Konsole steht, die von einem Putto gestützt wirdR. Schellenberg fec.Buchseite, vgl. die Zeichnung, Signatur: Colomba, Luca Antonio I, 1 (2) Pp A4 und die Druckgraphik, Signatur: Colomba, Luca Ant. I, 2Signiert im Bild links unten: "R. Schellenberg fec.
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