{"title":"A fully automated model for land use classification from historical maps using machine learning","authors":"Anneli M. Ågren, Yiqi Lin","doi":"10.1016/j.rsase.2024.101349","DOIUrl":null,"url":null,"abstract":"<div><p>Digital land use data before the age of satellites is scarce. Here, we build a machine learning model, using Extreme Gradient Boosting, that can automatically detect land use classes from an orthophoto map of Sweden (economic maps, 1:10 000 and 1:20 000) constructed from 1942 to 1988. Overall, the machine learning model demonstrated robust performance, with Cohen's Kappa and Matthews Correlation Coefficient of 0.86. The F1 values of the individual classes were 0.98, 0.95, 0.84, and 0.87 for graphics, arable land, forest, and open land, respectively. While the model can be used to detect land use changes in arable land, higher uncertainties associated with forest and open land necessitate further investigation at regional scales or exploration of improved mapping techniques. The code is publicly available to enable easy adaptation for classifying other historical maps.</p></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101349"},"PeriodicalIF":3.8000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938524002131","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Digital land use data before the age of satellites is scarce. Here, we build a machine learning model, using Extreme Gradient Boosting, that can automatically detect land use classes from an orthophoto map of Sweden (economic maps, 1:10 000 and 1:20 000) constructed from 1942 to 1988. Overall, the machine learning model demonstrated robust performance, with Cohen's Kappa and Matthews Correlation Coefficient of 0.86. The F1 values of the individual classes were 0.98, 0.95, 0.84, and 0.87 for graphics, arable land, forest, and open land, respectively. While the model can be used to detect land use changes in arable land, higher uncertainties associated with forest and open land necessitate further investigation at regional scales or exploration of improved mapping techniques. The code is publicly available to enable easy adaptation for classifying other historical maps.
期刊介绍：
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems
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