Computational Cartographic Recognition: Identifying Maps, Geographic Regions, and Projections from Images Using Machine Learning.
Map reading is a challenging task for computer programs. This article explores how artificial intelligence and machine learning methods can be used to understand maps, an area we broadly refer to as computational cartographic recognition. Specifically, we use machine learning methods to (1) identify...
| Publicado en: | Annals of the American Association of Geographers Vol. 113; no. 5; pp. 1243 - 1268 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2023
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=163888078&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 163888078 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2023 vid: 113 iid: 5 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 163888078 10.1080/24694452.2023.2166010 ppf: 1243 ppct: 25 formats: tig: atl: Computational Cartographic Recognition: Identifying Maps, Geographic Regions, and Projections from Images Using Machine Learning. aug: au: Li, Jialin Xiao, Ningchuan affil: Department of Geography, The Ohio State University, USA su: Artificial intelligence Machine learning Cartography Image recognition (Computer vision) Support vector machines Multilayer perceptrons Artificial neural networks sug: subj: Artificial intelligence Geophysical Surveying and Mapping Services Machine learning Cartography Image recognition (Computer vision) Support vector machines Multilayer perceptrons Artificial neural networks keyword: aprendizaje automático cartograma mapa reconocimiento cartográfico computacional red neuronal convolucional 卷积神经网络 变形地图 地图。 机器学习 计算地图识别 cartogram computational cartographic recognition convolutional neural network machine learning map aprendizaje automático cartograma mapa reconocimiento cartográfico computacional red neuronal convolucional 卷积神经网络 变形地图 地图。 机器学习 计算地图识别 cartogram computational cartographic recognition convolutional neural network machine learning map ab: Map reading is a challenging task for computer programs. This article explores how artificial intelligence and machine learning methods can be used to understand maps, an area we broadly refer to as computational cartographic recognition. Specifically, we use machine learning methods to (1) identify whether an image is a map, (2) recognize the geographic region on the map, and (3) recognize the projection used on the map. Four machine learning models—support vector machine, multilayer perceptrons, convolutional neural networks (CNNs) developed from scratch using our own architecture (CNNS), and pretrained CNN models through transfer learning (CNNT)—are applied in these tasks. We use 2,200 online map images, 500 nonmap images, and 1,050 synthetic map images to train and evaluate the models. Results show that the CNNT models achieve the highest performance among all models, with an accuracy rate above 90 percent for the tasks. The CNNS models come in second. We also conduct a round of stress tests using 3,600 additional synthetic maps where the shape and layout are systematically distorted and test if the models can still identify the maps and recognize the region and projection on the maps. The results of the stress tests show that the models can reliably recognize some of the modified maps even when exhibiting performance inferior to even random models for other maps. This unpredictable nature of the methods when applied to maps that are not represented in the training data suggests both promises and limitations of the current machine learning approaches to cartographic recognition. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|