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2011 3rd International Conference on Environmental Science and Information Application Technology(ESIAT 2011)

作者： Jiong You Jingxiong Zhang School of Remote Sensing and Information Engineering Wuhan University

As the main factor that influences classification quality, uncertainty characterization is analysis of area classes based on remotely sensed imagery and auxiliary data. This study focuses on uncertainty comparison between reference and classification maps. By referring to information classes and data classes respectively, experiment using real data sets was carried out to quantify uncertainty in area-class maps. Contingency tables and an information theory measure of shared information, percentage of average mutual information（%AMI）, were applied to compare the uncertainty between pairs of area-class maps, where maximum likelihood classifier was used to classify the image into area-class map with different reference data on the discriminant space. Results show that there exist large impacts of semantic bias in different reference on classification uncertainty. Therefore, further improving upon the effect of bias in reference data will be studied to enable a more accurate assessment of the quality of classification.

关键词： uncertainty comparison area-class maps reference data discriminant space average mutual information

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High-resolution land cover classification:cost-effective approach for extraction of reliable training data from existing land cover datasets

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International Journal of Digital Earth 2023年第1期16卷 3618-3636页

作者： Gorica Bratic Vasil Yordanov Maria Antonia Brovelli Politecnico di Milano MilanItaly

There has been a significant increase in the availability of global high-resolution land cover(HRLC)datasets due to growing demand and favorable technological advancements.However,this has brought forth the challenge of collecting reference data with a high level of detail for global extents.While photo-interpretation is considered optimal for collecting quality training data for global HRLC mapping,some producers of existing HRLCs use less trustworthy sources,such as existing land cover at a lower resolution,to reduce costs.This work proposes a methodology to extract the most accurate parts of existing HRLCs in response to the challenge of providing reliable reference data at a low cost.The methodology combines existing HRLCs by intersection,and the output represents a Map Of Land Cover Agreement(MOLCA)that can be utilized for selecting training samples.MOLCA’s effectiveness was demonstrated through HRLC map production in Africa,in which it generated 48,000 samples.The best classification test had an overall accuracy of 78%.This level of accuracy is comparable to or better than the accuracy of existing HRLCs obtained from more expensive sources of training data,such as photo-interpretation,highlighting the cost-effectiveness and reliability potential of the developed methodology in supporting global HRLC production.

关键词： High-resolution land cover global land cover training data reference data data quality

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A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

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Big Earth data 2021年第2期5卷 237-257页

作者： Amin Naboureh Jinhu Bian Guangbin Lei Ainong Li Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina University of Chinese Academy of Sciences BeijingChina

Large-scale projects,such as the construction of railways and highways,usually cause an extensive Land Use Land Cover Change(LULCC).The China-Central Asia-West Asia Economic Corridor(CCAWAEC),one key large-scale project of the Belt and Road Initiative(BRI),covers a region that is home to more than 1.6 billion people.Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor,reviewing LULCC mapping studies in this area has not been studied.This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area.The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor.To this end,350 peer-reviewed journal and conference papers,as well as book chapters were analyzed based on 17 attributes,such as main driving factors of LULCC,data collection methods,classification algorithms,and accuracy assessment methods.It was observed that:(1)rapid urbanization,industrialization,population growth,and climate change have been recognized as major causes of LULCC in the study area;(2)LULCC has,directly and indirectly,caused several environmental issues,such as biodiversity loss,air pollution,water pollution,desertification,and land degradation;(3)there is a lack of well-annotated national land use data in the region;(4)there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area;and(5)several technical issues still require more attention from the scientific community.Finally,several recommendations were proposed to address the identified issues.

关键词： Land use change land cover change China-Central Asia-West Asia Economic Corridor accuracy assessment reference data classification algorithm

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