[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124892-en":3,"doc-seo-124892-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124892,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Integrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring","Remotely sensed and geospatial big data are transforming land cover and land use mapping by leveraging the “3Vs” of data scale, diversity, and rapid arrival. The article explains how sources beyond satellite imagery—including authoritative datasets, citizen science, volunteered geographic information, mobile and low-cost sensors, and geotagged social media—can be integrated. It outlines machine learning and data fusion approaches to build new land cover/use datasets, supports change detection, and summarizes papers grouped by urban applications and temporal monitoring themes.","land  \nEditorial  \nIntegrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring  \nLinda See 1, *, Myroslava Lesiv 1 and Dmitry Schepaschenko 1,2  \nCitation: See, L.; Lesiv, M.; Schepaschenko, D. Integrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring. Land 2024, 13, 769. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)land13060769  \nReceived: 16 May 2024  \nAccepted: 28 May 2024  \nPublished: 29 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Novel Data Ecosystems for Sustainability (NODES) Group, International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria; [lesiv@iiasa.ac.at](lesiv@iiasa.ac.at) (M.L.); [schepd@iiasa.ac.at](schepd@iiasa.ac.at) (D.S.)  \n2 Agriculture, Forestry and Ecosystem Services (AFE) Group, International Institute for Applied Systems Analysis (IIASA), 2361 Laxenburg, Austria  \n* Correspondence: [see@iiasa.ac.at](see@iiasa.ac.at)  \n1. Introduction  \nThe last few decades have seen an explosion in the availability of remotely sensed and geospatial big data, which are defined by the 3 Vs: a large volume of data; a variety of different forms of data; and the rapid velocity of data arrival [1] . The term big data is particularly applicable to remote sensing. The opening of the Landsat archive [2], the spatially and temporally rich data now available from the Sentinel satellites [3], and the proliferation of small satellites photographing the Earth [4] all provide new opportunities for characterizing and monitoring the Earth’s surface.  \nNew sources of geospatial big data (as well as regular geospatial data) can also benefit the mapping and monitoring of land cover and land use. These include data from authoritative sources, e.g., data from official censuses and surveys, as well as data generated by citizens, both actively and passively. Citizen science [5] and volunteered geographic information [6] can provide data on land cover and use through initiatives such as OpenStreetMap (OSM) [7], Geo-Wiki [8], and many other projects that involve volunteers monitoring the environment or landscape features. Mobile phones and low-cost sensors can provide new streams of information through mobile apps that facilitate data collection [9] or that collect information in the background [10], as well as a variety of different sensors that are being used for environmental monitoring [11,12] . Data from social media, including geotagged photographs from sites such as Flickr or street-level photographs from providers such as Google Street View and Mapillary, can be processed using computer vision and segmentation to extract information related to land cover and land use [13,14] .  \nThe logical progression of this field of study is the integration of remote sensing with these different sources of geospatial data using various machine learning and data fusion approaches to create new data sets on land cover and land use. Much of the previous integration work in this area has focused on urban areas because of the large number of geospatial data sets available for cities [15] . Yet, there is considerable potential for creating better data sets in other domains as well. One example is the mapping of land use intensities, which involved integrating Corine land cover and other remotely sensed datasets with statistical and other geospatial data sources [16] to produce a map for Europe with a 1 km resolution. Another example is the recently produced global map of forest management [17], which used data crowdsourced via the Geo-Wiki platform to train aclassifier with satellite ima","cbCainVscFcGJTxE","https://ap.wps.com/l/cbCainVscFcGJTxE","pdf",180739,1,6,"English","en",105,"# Introduction\n## Overview of Papers in the Special Issue\n### Urban Applications","[{\"question\":\"Why is big data especially relevant to remote sensing?\",\"answer\":\"Big data is particularly applicable to remote sensing because large volumes of data are generated, in diverse forms, and arrive at high velocity, enabling richer characterization of Earth’s surface.\"},{\"question\":\"What kinds of geospatial data sources can support land cover and land use mapping?\",\"answer\":\"The document highlights satellite archives and Sentinel data, authoritative sources like censuses and surveys, citizen science and volunteered geographic information (e.g., OpenStreetMap and Geo-Wiki), mobile apps and low-cost sensors, and social media geotagged imagery processed via computer vision.\"},{\"question\":\"How does the special issue structure its included studies?\",\"answer\":\"The special issue contains nine papers grouped into three main themes: urban applications, monitoring and understanding changes over time, and other land cover and land use application domains.\"}]","Integrating Remote Sensing and Geospatial Big Data for Land Cover and Land Use Mapping and Monitoring | PDF",1785895258,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-remote-sensing-and-geospatial-big-data-for-land-cover-and-land-use-mapping-and-monitoring","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/integrating-remote-sensing-and-geospatial-big-data-for-land-cover-and-land-use-mapping-and-monitoring/124892/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is big data especially relevant to remote sensing?","Question",{"text":75,"@type":76},"Big data is particularly applicable to remote sensing because large volumes of data are generated, in diverse forms, and arrive at high velocity, enabling richer characterization of Earth’s surface.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What kinds of geospatial data sources can support land cover and land use mapping?",{"text":80,"@type":76},"The document highlights satellite archives and Sentinel data, authoritative sources like censuses and surveys, citizen science and volunteered geographic information (e.g., OpenStreetMap and Geo-Wiki), mobile apps and low-cost sensors, and social media geotagged imagery processed via computer vision.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the special issue structure its included studies?",{"text":84,"@type":76},"The special issue contains nine papers grouped into three main themes: urban applications, monitoring and understanding changes over time, and other land cover and land use application domains.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]