Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture...Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.展开更多
The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agricul...The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal changes.In this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical analytics.This represents a new systematic validation approach for semantic image content verification.We will focus on a number of different scenarios proposed by the user community using Sentinel data.From a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring.展开更多
Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being ca...Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being capable of handling the size,complexity and variety of data,are required.Data scientists require support for the data manipulation,labeling and information extraction processes.This paper presents our Earth Observation Image Librarian(EOLib),a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data,in general.The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments(PGS).EOLib is composed of several modules which offer functionalities such as data ingestion,feature extraction from SAR(Synthetic Aperture Radar)data,meta-data extraction,semantic definition of the image content through machine learning and data mining methods,advanced querying of the image archives based on content,meta-data and semantic categories,as well as 3-D visualization of the processed images.EOLib is operated by DLR’s(German Aerospace Center’s)Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen,Germany.展开更多
文摘Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.
基金The work was supported by the European Commission’s H2020 CANDELA project under Grant Agreement No.776193.
文摘The increased number of free and open Sentinel satellite images has led to new applications of these data.Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images,in particular,the identification and quantification of their temporal changes.In this paper,we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes,and presenting these as classification maps and/or statistical analytics.This represents a new systematic validation approach for semantic image content verification.We will focus on a number of different scenarios proposed by the user community using Sentinel data.From a large number of potential use cases,we selected three main cases,namely forest monitoring,flood monitoring,and macro-economics/urban monitoring.
基金The work was supported by EOLib—an ESA technological project ESA EOLib project,2019.
文摘Throughout the years,various Earth Observation(EO)satellites have generated huge amounts of data.The extraction of latent information in the data repositories is not a trivial task.New methodologies and tools,being capable of handling the size,complexity and variety of data,are required.Data scientists require support for the data manipulation,labeling and information extraction processes.This paper presents our Earth Observation Image Librarian(EOLib),a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data,in general.The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments(PGS).EOLib is composed of several modules which offer functionalities such as data ingestion,feature extraction from SAR(Synthetic Aperture Radar)data,meta-data extraction,semantic definition of the image content through machine learning and data mining methods,advanced querying of the image archives based on content,meta-data and semantic categories,as well as 3-D visualization of the processed images.EOLib is operated by DLR’s(German Aerospace Center’s)Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen,Germany.
