Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due...Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.展开更多
Urbanization is one of the most important social and economic phenomena in the world today.This paper reviews the formation of megacities and summarizes the main problems,challenges and opportunities faced by the sust...Urbanization is one of the most important social and economic phenomena in the world today.This paper reviews the formation of megacities and summarizes the main problems,challenges and opportunities faced by the sustainable development of such large megacities.Issues discussed include the problems of land subsidence in megacities,environmental issues,traffic problems and energy supply aspects.The sustainable development of megacities in other parts of the world provided the references and experiences for the countermeasures of megacity planning and development in China.The vision of Digital Earth and Digital Cities can play a major role in the efficient management and sustainable growth of such megacities all around the world.展开更多
The vision of a Digital Earth calls for more dynamic information systems,new sources of information,and stronger capabilities for their integration.Sensor networks have been identified as a major information source fo...The vision of a Digital Earth calls for more dynamic information systems,new sources of information,and stronger capabilities for their integration.Sensor networks have been identified as a major information source for the Digital Earth,while Semantic Web technologies have been proposed to facilitate integration.So far,sensor data are stored and published using the Observations&Measurements standard of the Open Geospatial Consortium(OGC)as data model.With the advent of Volunteered Geographic Information and the Semantic Sensor Web,work on an ontological model gained importance within Sensor Web Enablement(SWE).In contrast to data models,an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain.Ontologies restrict the interpretation of vocabularies toward their intended meaning.The ongoing paradigm shift to Linked Sensor Data complements this attempt.Two questions have to be addressed:(1)how to refer to changing and frequently updated data sets using Uniform Resource Identifiers,and(2)how to establish meaningful links between those data sets,that is,observations,sensors,features of interest,and observed properties?In this paper,we present a Linked Data model and a RESTful proxy for OGC’s Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.展开更多
基金Foundation of Anhui Province Key Laboratory of Physical Geographic Environment(No.2022PGE012)
文摘Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers,managers,and policymakers in terms of better managing and utilizing their agricultural resources.Due to their small size,irregular shape,and the use of mixed-cropping techniques,the farm fields of smallholder can be difficult to delineate automatically.In recent years,numerous studies on field contour extraction using a deep Convolutional Neural Network(CNN)have been proposed.However,there is a relative shortage of labeled data for filed boundaries,thus affecting the training effect of CNN.Traditional methods mostly use image flipping,and random rotation for data augmentation.In this paper,we propose to apply Generative Adversarial Network(GAN)for the data augmentation of farm fields label to increase the diversity of samples.Specifically,we propose an automated method featured by Fully Convolutional Neural networks(FCN)in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution(VHR)images.We first investigate four State-Of-The-Art(SOTA)FCN architectures,i.e.,U-Net,PSPNet,SegNet and OCRNet,to find the optimal architecture in the contour detection task of smallholder farm fields.Second,we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection.We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria.The best combination achieved F1 scores of 0.686 on Test Set 1(TS1),0.684 on Test Set 2(TS2),and 0.691 on Test Set 3(TS3).Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task.The conceptual,theoretical,and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.
基金the National Key R&D plan on Strategic International Scientific and Technological Innovation Cooperation special project(2016YFE0202300)the Wuhan Chen Guang project(2016070204010114)+2 种基金the Guangzhou Science and Technology project(201604020070)the Special Task of Technical Innovation in Hubei Province project(2016AAA018)the Natural Science Foundation of China(61671332,41771452 and 41771454).
文摘Urbanization is one of the most important social and economic phenomena in the world today.This paper reviews the formation of megacities and summarizes the main problems,challenges and opportunities faced by the sustainable development of such large megacities.Issues discussed include the problems of land subsidence in megacities,environmental issues,traffic problems and energy supply aspects.The sustainable development of megacities in other parts of the world provided the references and experiences for the countermeasures of megacity planning and development in China.The vision of Digital Earth and Digital Cities can play a major role in the efficient management and sustainable growth of such megacities all around the world.
基金The presented work is developed within the 528 North semantics community,and partly funded by the European projects UncertWeb(FP7-248488)ENVISION(FP7-249170)the GENESIS project(an Integrated Project,contract number 223996).
文摘The vision of a Digital Earth calls for more dynamic information systems,new sources of information,and stronger capabilities for their integration.Sensor networks have been identified as a major information source for the Digital Earth,while Semantic Web technologies have been proposed to facilitate integration.So far,sensor data are stored and published using the Observations&Measurements standard of the Open Geospatial Consortium(OGC)as data model.With the advent of Volunteered Geographic Information and the Semantic Sensor Web,work on an ontological model gained importance within Sensor Web Enablement(SWE).In contrast to data models,an ontological approach abstracts from implementation details by focusing on modeling the physical world from the perspective of a particular domain.Ontologies restrict the interpretation of vocabularies toward their intended meaning.The ongoing paradigm shift to Linked Sensor Data complements this attempt.Two questions have to be addressed:(1)how to refer to changing and frequently updated data sets using Uniform Resource Identifiers,and(2)how to establish meaningful links between those data sets,that is,observations,sensors,features of interest,and observed properties?In this paper,we present a Linked Data model and a RESTful proxy for OGC’s Sensor Observation Service to improve integration and inter-linkage of observation data for the Digital Earth.
