The potential of citizens as a source of geographical information has been recognized for many years.Such activity has grown recently due to the proliferation of inexpensive location aware devices and an ability to sh...The potential of citizens as a source of geographical information has been recognized for many years.Such activity has grown recently due to the proliferation of inexpensive location aware devices and an ability to share data over the internet.Recently,a series of major projects,often cast as citizen observatories,have helped explore and develop this potential for a wide range of applications.Here,some of the experiences and learnings gained from part of one such project,which aimed to further the role of citizen science within Earth observation and help address environmental challenges,LandSense,are shared.The key focus is on quality assurance of citizen generated data on land use and land cover especially to support analyses of remotely sensed data and products.Particular focus is directed to quality assurance checks on photographic image quality,privacy,polygon overlap,positional accuracy and offset,contributor agreement,and categorical accuracy.The discussion aims to provide good practice advice to aid future studies and help fulfil the full potential of citizens as a source of volunteered geographical information(VGI).展开更多
In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,i...In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles.The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples.Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.The final dehazed image is yielded after iterations of the high-pass filter.STRASS can be run directly without any machine learning.Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts.Image dehazing can be applied in the field of printing and packaging,our method is of great significance for image pre-processing before printing.展开更多
Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline lea...Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and hu</span><span style="font-family:Verdana;">man habitats. This research, basing on geospatial techniques, focuses on coastal </span><span style="font-family:Verdana;">erosion monitoring from 1988 to 2018 in Togo. It is interested in the extrac</span><span style="font-family:Verdana;">tion of shoreline and in the analysis of change. Various satellite images index</span></span><span style="font-family:Verdana;">es</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of </span><span style="font-family:Verdana;">Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Supp</span><span style="font-family:Verdana;">ort Vector Machine (SVM) Supervised Classification methods for the</span><span style="font-family:Verdana;"> extraction of the shoreline on NDWI index. The topographic morphology such </span><span style="font-family:Verdana;">as linear and non-linear coastal surfaces have been considered. The estimation</span><span style="font-family:Verdana;"> of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised </span><span style="font-family:Verdana;">Classification method showed good performance on linear and non-linear coastal </span><span style="font-family:Verdana;">surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population</span><b> </b><span style="font-family:Verdana;">and disturbance of human habitats.展开更多
The present study aimed to provide the basics needed to reconcile the fight against poverty with the need of a good management of community resources in the context of local and sustainable development. It focused on ...The present study aimed to provide the basics needed to reconcile the fight against poverty with the need of a good management of community resources in the context of local and sustainable development. It focused on the identification of geographic location and spatio-temporal dynamics of these resources in the southeastern part of Togo. The methodological approach was based on the spatial analysis of the area for the years 1988, 2000 and 2018 by combining optical imagery from Landsat TM, ETM+ and Sentinel-2A MSI satellites with radar imagery from Sentinel-1A IW/GRDH. Spatial analysis showed a fragmented spatial structure undergoing major changes for the period 1988-2018. Plantations, riparian formations, agglomerations, water bodies and wetlands increased respectively by 4.61%, 2.09%, 1.07%, 0.43% and 0.35% annually, while forests, savannahs, crops and fallow lands decreased annually by 0.71%, 5.87% and 1.97%. For the “forests” class, seven community forests geographically organized in three sub-groups were identified and mapped. The analysis of their areas pointed to the fact that out of 667 ha of community forests in 1988, only 415 ha remain in 2018, which means a loss of 37.78% in forest areas over the 30 years, or an annual deforestation rate of 1.64%. The different spatial changes observed could be attributed to several unsustainable human activities. The land use maps for the years 1988, 2000 and 2018 will contribute to resource localization and protection in sensitive areas or, in other words, to the integrated and rational management of these resources. The different identified and mapped community forests could serve as management units for managers in developing their management plans.展开更多
Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a gro...Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a growing interest in using existing VGI to update authoritative databases.This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique,in order to update an authoritative land use database.Each VGI data source is considered to be an independent source of information,which is fused together using Dempster-Shafer Theory(DST).The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency.Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles.The data fusion approach achieved an overall accuracy of 85.6%for the 144 features having at least two contributions when the confidence threshold was set to 0.05.Despite the heterogeneity and limited amount of VGI used,the results are promising,with 99%of the LU polygons updated or enriched.These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally。展开更多
基金funded by the European Commission’s Horizon 2020 program as part of the LandSense project[grant number 689812]Horizon 2020[LandSense,689812]。
文摘The potential of citizens as a source of geographical information has been recognized for many years.Such activity has grown recently due to the proliferation of inexpensive location aware devices and an ability to share data over the internet.Recently,a series of major projects,often cast as citizen observatories,have helped explore and develop this potential for a wide range of applications.Here,some of the experiences and learnings gained from part of one such project,which aimed to further the role of citizen science within Earth observation and help address environmental challenges,LandSense,are shared.The key focus is on quality assurance of citizen generated data on land use and land cover especially to support analyses of remotely sensed data and products.Particular focus is directed to quality assurance checks on photographic image quality,privacy,polygon overlap,positional accuracy and offset,contributor agreement,and categorical accuracy.The discussion aims to provide good practice advice to aid future studies and help fulfil the full potential of citizens as a source of volunteered geographical information(VGI).
基金This work was supported in part by National Natural Science Foundation of China under Grant 62076199in part by the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety under Grant BTBD-2020KF08+2 种基金Beijing Technology and Business University,in part by the China Postdoctoral Science Foundation under Grant 2019M653784in part by Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences under Grant LSIT201801Din part by the Key R&D Project of Shaan’xi Province under Grant 2021GY-027。
文摘In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles.The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples.Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.The final dehazed image is yielded after iterations of the high-pass filter.STRASS can be run directly without any machine learning.Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts.Image dehazing can be applied in the field of printing and packaging,our method is of great significance for image pre-processing before printing.
文摘Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and hu</span><span style="font-family:Verdana;">man habitats. This research, basing on geospatial techniques, focuses on coastal </span><span style="font-family:Verdana;">erosion monitoring from 1988 to 2018 in Togo. It is interested in the extrac</span><span style="font-family:Verdana;">tion of shoreline and in the analysis of change. Various satellite images index</span></span><span style="font-family:Verdana;">es</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of </span><span style="font-family:Verdana;">Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Supp</span><span style="font-family:Verdana;">ort Vector Machine (SVM) Supervised Classification methods for the</span><span style="font-family:Verdana;"> extraction of the shoreline on NDWI index. The topographic morphology such </span><span style="font-family:Verdana;">as linear and non-linear coastal surfaces have been considered. The estimation</span><span style="font-family:Verdana;"> of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised </span><span style="font-family:Verdana;">Classification method showed good performance on linear and non-linear coastal </span><span style="font-family:Verdana;">surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population</span><b> </b><span style="font-family:Verdana;">and disturbance of human habitats.
文摘The present study aimed to provide the basics needed to reconcile the fight against poverty with the need of a good management of community resources in the context of local and sustainable development. It focused on the identification of geographic location and spatio-temporal dynamics of these resources in the southeastern part of Togo. The methodological approach was based on the spatial analysis of the area for the years 1988, 2000 and 2018 by combining optical imagery from Landsat TM, ETM+ and Sentinel-2A MSI satellites with radar imagery from Sentinel-1A IW/GRDH. Spatial analysis showed a fragmented spatial structure undergoing major changes for the period 1988-2018. Plantations, riparian formations, agglomerations, water bodies and wetlands increased respectively by 4.61%, 2.09%, 1.07%, 0.43% and 0.35% annually, while forests, savannahs, crops and fallow lands decreased annually by 0.71%, 5.87% and 1.97%. For the “forests” class, seven community forests geographically organized in three sub-groups were identified and mapped. The analysis of their areas pointed to the fact that out of 667 ha of community forests in 1988, only 415 ha remain in 2018, which means a loss of 37.78% in forest areas over the 30 years, or an annual deforestation rate of 1.64%. The different spatial changes observed could be attributed to several unsustainable human activities. The land use maps for the years 1988, 2000 and 2018 will contribute to resource localization and protection in sensitive areas or, in other words, to the integrated and rational management of these resources. The different identified and mapped community forests could serve as management units for managers in developing their management plans.
基金supported by Horizon 2020 Framework Programme[grant number 689812].
文摘Updating an authoritative Land Use and Land Cover(LULC)database requires many resources.Volunteered geographic information(VGI)involves citizens in the collection of data about their spatial environment.There is a growing interest in using existing VGI to update authoritative databases.This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique,in order to update an authoritative land use database.Each VGI data source is considered to be an independent source of information,which is fused together using Dempster-Shafer Theory(DST).The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency.Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles.The data fusion approach achieved an overall accuracy of 85.6%for the 144 features having at least two contributions when the confidence threshold was set to 0.05.Despite the heterogeneity and limited amount of VGI used,the results are promising,with 99%of the LU polygons updated or enriched.These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally。
