The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;data-CASE: grounding data regulations for compliant data processing s...
ISBN:
(纸本)9783893180950
The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;data-CASE: grounding data regulations for compliant data processing systems;data coverage for detecting representation bias in imagedatasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.
The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information ...
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The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;data-CASE: grounding data regulations for compliant data processing s...
ISBN:
(纸本)9783893180943
The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;data-CASE: grounding data regulations for compliant data processing systems;data coverage for detecting representation bias in imagedatasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.
The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;data-CASE: grounding data regulations for compliant data processing s...
ISBN:
(纸本)9783893180943
The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;data-CASE: grounding data regulations for compliant data processing systems;data coverage for detecting representation bias in imagedatasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.
The proceedings contain 39 papers. The special focus in this conference is on Unmanned Aerial System in geomatics. The topics include: Drone Technology in Waste Management: A Review;Solar Roof Panel Extraction from UA...
ISBN:
(纸本)9783031193088
The proceedings contain 39 papers. The special focus in this conference is on Unmanned Aerial System in geomatics. The topics include: Drone Technology in Waste Management: A Review;Solar Roof Panel Extraction from UAV Photogrammetric Point Cloud;Spacio-Statistical Model to Predict Crime Locations Based on Past Crime Events and UAV Based Monitoring of the Predicted Surveillance Route;Automatic Ship Detection Using CFAR Algorithm for Quad-Pol UAV-SAR imagery;A Deep Learning Approach for Detection and Segmentation of Airplanes in Ultrahigh-spatial-Resolution UAV dataset;Influence of European UAS Regulations on image Acquisition for 3D Building Modeling;Effects of Flight Plan Parameters on the Quality and Usability of Low-Cost UAS Photogrammetry data Products for Tree Crown Delineation;The Segmentation of Drone image derived 3D Point Cloud Using a Combination of RANSAC, DBSCAN and Clustering Methods;UAV to Cadastral Parcel Boundary Translation and Synthetic UAV image Generation Using Conditional-Generative Adversarial Network;An automated Process to Filter UAS-Based Point Clouds;some Enhancement of Aerial and Terrestrial Photo for 3D Modeling of Texture-Less Object Surface;role of Drone Technology in Sustainable Rural Development: Opportunities and Challenges;disaster Risk Mapping from Aerial imagery Using Deep Learning Techniques;High-Resolution Mapping of Forest Canopy Cover Using UAV and Sentinel-2;design and Method of an Agricultural Drone System Using Biomass Vegetation Indices and Multispectral images;UAV-LiDAR and Terrestrial Laser Scanning for Automatic Extraction of Forest Inventory Parameters;A UAS-Based Approach for Orchard geo-Information Management System;High-Speed Wi-Fi Systems for Long Range FANETS: Real Problems, Experiments, and Lessons Learnt;preface.
The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information ...
The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods.
The proceedings contain 27 papers. The special focus in this conference is on Soft Computing, Optimization Theory and Applications. The topics include: Drone Technology in Waste Management: A Review;Solar Roof Panel E...
ISBN:
(纸本)9789811964053
The proceedings contain 27 papers. The special focus in this conference is on Soft Computing, Optimization Theory and Applications. The topics include: Drone Technology in Waste Management: A Review;Solar Roof Panel Extraction from UAV Photogrammetric Point Cloud;Spacio-Statistical Model to Predict Crime Locations Based on Past Crime Events and UAV Based Monitoring of the Predicted Surveillance Route;Automatic Ship Detection Using CFAR Algorithm for Quad-Pol UAV-SAR imagery;A Deep Learning Approach for Detection and Segmentation of Airplanes in Ultrahigh-spatial-Resolution UAV dataset;Influence of European UAS Regulations on image Acquisition for 3D Building Modeling;Effects of Flight Plan Parameters on the Quality and Usability of Low-Cost UAS Photogrammetry data Products for Tree Crown Delineation;The Segmentation of Drone image derived 3D Point Cloud Using a Combination of RANSAC, DBSCAN and Clustering Methods;UAV to Cadastral Parcel Boundary Translation and Synthetic UAV image Generation Using Conditional-Generative Adversarial Network;An automated Process to Filter UAS-Based Point Clouds;some Enhancement of Aerial and Terrestrial Photo for 3D Modeling of Texture-Less Object Surface;role of Drone Technology in Sustainable Rural Development: Opportunities and Challenges;disaster Risk Mapping from Aerial imagery Using Deep Learning Techniques;High-Resolution Mapping of Forest Canopy Cover Using UAV and Sentinel-2;design and Method of an Agricultural Drone System Using Biomass Vegetation Indices and Multispectral images;UAV-LiDAR and Terrestrial Laser Scanning for Automatic Extraction of Forest Inventory Parameters;A UAS-Based Approach for Orchard geo-Information Management System;High-Speed Wi-Fi Systems for Long Range FANETS: Real Problems, Experiments, and Lessons Learnt;preface.
GREENPEG is a H2020 project developing exploration toolsets for critical materials in pegmatites. Lineament mapping is a powerful tool to identify geological structures at different scales, which is commonly applied i...
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ISBN:
(纸本)9781510655409;9781510655393
GREENPEG is a H2020 project developing exploration toolsets for critical materials in pegmatites. Lineament mapping is a powerful tool to identify geological structures at different scales, which is commonly applied in mineral exploration. To extract lineament information, different types of data can be employed, namely: (i) optical remote sensing data, (ii) radar remote sensing data, and (iii) digital elevation models (DEM5). All these data have been used by GREENPEG, where image processing of Sentinel-1 and ALOS (DEM) datasets were evaluated to identify regional-scale tectonic structures, such as faults, that may have controlled pegmatite melt emplacement. automatedgeomatics methods to extract lineament information and perform lineament mapping were employed and the mean direction of the extracted lineaments was evaluated through rose diagrams. In general, the results of the radar image processing were affected by noise in the coastal areas and by the topography of the region. ALOS data presented some advantages, due to less influence from topography and human structures (saving time in the visual inspection of the results). Moreover, even though higher thresholds for the minimum length to consider as a lineament were applied to Sentinel-1 images, ALOS-derived lineaments were longer. The main trend of the extracted lineaments is in agreement between the two datasets, although specific trends are clearer in the ALOS-derived lineaments because the shaded reliefs produced already filtered the azimuth of lineaments to be extracted according to light incidence angle. For all three project demonstration sites (Leinster, Ireland;Wolfsberg, Austria;and Tysfjord, Norway) it was possible to identify lineaments that may relate to structures that controlled pegmatite emplacement. This study demonstrates the usefulness of different satellite data in structural mapping and mineral exploration.
Studies of wildlife species distribution patterns are increasingly important in the face of rapid ecosystem changes that have implications for disease emergence and spread, food security, climate change, and invasive ...
Studies of wildlife species distribution patterns are increasingly important in the face of rapid ecosystem changes that have implications for disease emergence and spread, food security, climate change, and invasive species biology. Citizen science campaigns can be very effective for observing wildlife behaviour, but they can also be a resource-consuming process and limited in coverage and sometimes their accuracy. Due to their wide usage, social media platforms represent an untapped source of potentially valuable wildlife observational data which is less costly to obtain but could complement citizen science data collections and support real-time species monitoring and analysis. There are however concerns about the correctness and completeness of social media data sources. Further, the exploitation of social media data related to wildlife involves challenges such as its heterogeneity, noisiness and lack of adequate labelled data. Previous research on using social media sites in ecology studies is limited and often involves manual or semi-automated approaches with few attempts to exploit advanced machine learning methods.
In this thesis, we aim to identify social media mining techniques that facilitate the us-age of social media datasets as a source of wildlife observational data. First, we study the potential of social media data to supplement citizen science data collections and perform a range of statistical, spatial, and temporal analyses. We also present image and text-classification based verification approaches for identifying wildlife observations on social media which are suitable for large and diverse data collections. To address the fact the only a small proportion of social media posts have coordinates, we develop geo-referencing techniques that use state-of-the-art transformer-based neural network models, transfer learning, and regression models. These methods are extended with hybrid approaches incorporating machine learning and rule-b
The significance of forest ecosystems in terms of ecosystem processes and services and impacts on humanity is fully acknowledged. The constant exploitation of natural resources and the increasing anthropogenic pressur...
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ISBN:
(数字)9781510630628
ISBN:
(纸本)9781510630611;9781510630628
The significance of forest ecosystems in terms of ecosystem processes and services and impacts on humanity is fully acknowledged. The constant exploitation of natural resources and the increasing anthropogenic pressure on ecosystems continue to put a strain on and irretrievably threaten global forest ecosystems. Global forest health is declining due to climate change, air pollution and increased human activities. Protecting and monitoring the health of forest ecosystems is a vital resource management function. The technological development in the field of remote sensing provides new tools and automated solutions for forest health monitoring. An effective web-based forest health monitoring platform can contribute to ecological, social, and economic aspects. This study aims to design rapid and automated workflows (spatial Models-SMs) for time-series forest health monitoring with flexible parameterization and user-friendly interfaces ready for feeding WPS web-GIS platforms. Those include: i) SMs that ingest available time-series data and perform preprocessing activities, ii) SMs that calculate time-series of vegetation, soil and water indices from multispectral optical imagery, iii) SMs that create colored composite images from image algebra and SAR polarizations and vi) SMs that extract change detection maps from time-series SAR data. The study area is located in the wider region of the Mouzaki, Greece, where various types of forest species can be found. Sentinel-1 & 2 data were used while the ERDAS IMAGINE software was utilized for the design of the SMs. The results indicate the potential of the designed SMs to feed WPS webGIS platforms promptly and efficiently.
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