Bibliography

Introduction

This bibliography offers a starting point for exploring GeoAI research, encompassing key publications, textbooks, and online resources. Consider it a living document, constantly evolving as the field progresses.

Core Books

Gao, S. et al. Handbook of Geospatial Artificial Intelligence. 2023
Chiang, Y.-Y. et al. Training Deep Learning Models for Geographic Feature Recognition from Historical Maps. 2020

Core Articles

Hu, Y. et al. A five-year milestone: reflections on advances and limitations in GeoAI research. 2024
Kang, Y. et al. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics. 2024
Harrie, L. et al. Machine learning in cartography. 2024
Robinson, A.C. et al. Cartography in GeoAI: Emerging Themes and Research Challenges. 2023
Chen, M. et al. Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges. 2023
Elizar, E. et al. A Review on Multiscale-Deep-Learning Applications. 2022
Li, W. et al. GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography. 2022
Usery, E.L. et al. GeoAI in the US Geological Survey for topographic mapping. 2022
Li, W. GeoAI: Where machine learning and big data converge in GIScience. 2020
Janowicz, K. et al. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. 2020
Hu, Y. et al. GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions. 2019

Map Distinction

Li, J. Computational Cartographic Recognition: Exploring the Use of Machine Learning and Other Computational Approaches to Map Reading. 2022
Schnürer, R. et al. Detection of Pictorial Map Objects with Convolutional Neural Networks. 2021

Map Localisation

Oh, B.-W. Map Detection using Deep Learning. 2020

Feature Extraction (Points)

Vassányi, G. et al. Automatic vectorization of point symbols on archive maps using deep convolutional neural network. 2021
Guo, M. et al. Deep learning framework for geological symbol detection on geological maps. 2021
Kong, Y. et al. A Mountain Summit Recognition Method Based on Improved Faster R-CNN. 2021
Saeedimoghaddam, M. et al. Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks. 2020
Torres, R.N. et al. A Deep Learning Model for Identifying Mountain Summits in Digital Elevation Model Data. 2018

Feature Extraction (Lines)

Zhao, Y. et al. AU3-GAN: A Method for Extracting Roads from Historical Maps Based on an Attention Generative Adversarial Network. 2024
Jiao, C. et al. A novel framework for road vectorization and classification from historical maps based on deep learning and symbol painting. 2024
Xia, X. et al. Contrastive Pretraining for Railway Detection: Unveiling Historical Maps with Transformers. 2023
Wu, S. et al. Leveraging uncertainty estimation and spatial pyramid pooling for extracting hydrological features from scanned historical topographic maps. 2022
Jiao, C. et al. A fast and effective deep learning approach for road extraction from historical maps by automatically generating training data with symbol reconstruction. 2022
Ran, W. et al. Raster Map Line Element Extraction Method Based on Improved U-Net Network. 2022
Jiao, C. et al. A Novel Data Augmentation Method to Enhance the Training Dataset for Road Extraction from Swiss Historical Maps. 2022
Avcı, C. et al. Deep Learning-Based Road Extraction From Historical Maps. 2022
Mao, X. et al. Deep learning-enhanced extraction of drainage networks from digital elevation models. 2021
Ekim, B. et al. Automatic Road Extraction from Historical Maps Using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II Map. 2021
Satari, R. et al. Extraction of linear structures from digital terrain models using deep learning. 2021
Yang, X. et al. T2I-CycleGAN: A CycleGAN for Maritime Road Network Extraction from Crowdsourcing Spatio-Temporal AIS Trajectory Data. 2021
Petitpierre, R. et al. Generic Semantic Segmentation of Historical Maps. 2021
Ma, J. et al. Automatic identification method of overpasses based on deep learning. 2020

Feature Extraction (Polygons)

Xia, X. et al. Vectorizing historical maps with topological consistency: A hybrid approach using transformers and contour-based instance segmentation. 2024
Šanca, S. et al. AN END-TO-END DEEP LEARNING WORKFLOW FOR BUILDING SEGMENTATION, BOUNDARY REGULARIZATION AND VECTORIZATION OF BUILDING FOOTPRINTS. 2023
Mai, G. et al. Towards general-purpose representation learning of polygonal geometries. 2023
Xydas, C. et al. Buildings Extraction from Historical Topographic Maps via a Deep Convolution Neural Network. 2022
Farmakis-Serebryakova, M. et al. Terrain Segmentation Using a U-Net for Improved Relief Shading. 2022
Du, K. et al. Comparison of RetinaNet-Based Single-Target Cascading and Multi-Target Detection Models for Administrative Regions in Network Map Pictures. 2022
Soliman, A. et al. Weakly Supervised Segmentation of Buildings in Digital Elevation Models. 2022
Xie, X. et al. Building Function Recognition Using the Semi-Supervised Classification. 2022
Schnürer, R. et al. Instance Segmentation, Body Part Parsing, and Pose Estimation of Human Figures in Pictorial Maps. 2022
Wu, J. et al. An Automatic Extraction Method for Hatched Residential Areas in Raster Maps Based on Multi-Scale Feature Fusion. 2021
Schnürer, R. et al. Detection of Pictorial Map Objects with Convolutional Neural Networks. 2021
Chen, Y. et al. Vectorization of Historical Maps Using Deep Edge Filtering and Closed Shape Extraction. 2021
Chen, Y. et al. Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation. 2021
Petitpierre, R. et al. Generic Semantic Segmentation of Historical Maps. 2021
Garcia-Molsosa, A. et al. Potential of deep learning segmentation for the extraction of archaeological features from historical map series. 2021
Maxwell, A.E. et al. Semantic Segmentation Deep Learning for Extracting Surface Mine Extents from Historic Topographic Maps. 2020
Heitzler, M. et al. Cartographic reconstruction of building footprints from historical maps: A study on the Swiss Siegfried map. 2020

Feature Extraction (Labels)

Arundel, S. et al. Deep learning detection and recognition of spot elevations on historic topographic maps. 2022
Can, Y.S. et al. Text Detection and Recognition by using CNNs in the Austro-Hungarian Historical Military Mapping Survey. 2021
Garcia-Molsosa, A. et al. Potential of deep learning segmentation for the extraction of archaeological features from historical map series. 2021
Weinman, J. et al. Deep Neural Networks for Text Detection and Recognition in Historical Maps. 2019

Feature Extraction (Fuzzy Elements)

Wu, S. et al. A Closer Look at Segmentation Uncertainty of Scanned Historical Maps. 2022
Ståhl, N. et al. Identifying wetland areas in historical maps using deep convolutional neural networks. 2022
Uhl, J.H. et al. Automated Extraction of Human Settlement Patterns From Historical Topographic Map Series Using Weakly Supervised Convolutional Neural Networks. 2020
Uhl, J.H. et al. Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing. 2018
Uhl, J.H. et al. Extracting human settlement footprint from historical topographic map series using context-based machine learning. 2017

Pattern Detection (Lines)

Li, P. et al. MultiLineStringNet: a deep neural network for linear feature set recognition. 2024
Xu, Y. et al. Application of a graph convolutional network with visual and semantic features to classify urban scenes. 2022
Yang, M. et al. Pattern Recognition and Segmentation of Administrative Boundaries Using a One-Dimensional Convolutional Neural Network and Grid Shape Context Descriptor. 2022
Yang, M. et al. Detecting interchanges in road networks using a graph convolutional network approach. 2022
Djouvas, C. et al. Automating road junction identification using Crowdsourcing and Machine Learning on GPS transformed data. 2021
Kuo, C.-L. et al. Road Characteristics Detection Based on Joint Convolutional Neural Networks with Adaptive Squares. 2021
Touya, G. et al. Deep Learning for Enrichment of Vector Spatial Databases: Application to Highway Interchange. 2020
Li, C. et al. A complex junction recognition method based on GoogLeNet model. 2020
Li, H. et al. Automatic Identification of Overpass Structures: A Method of Deep Learning. 2019
He H. et al. Interchange Recognition Method Based on CNN. 2018

Pattern Detection (Polygons)

Li, P. et al. MultiLineStringNet: a deep neural network for linear feature set recognition. 2024
Xu, Y. et al. Application of a graph convolutional network with visual and semantic features to classify urban scenes. 2022
Yan, X. et al. A graph deep learning approach for urban building grouping. 2022
Ma, L. et al. A New Graph-Based Fractality Index to Characterize Complexity of Urban Form. 2022
Hu, Y. et al. Few-Shot Building Footprint Shape Classification with Relation Network. 2022
Li, Y. et al. A Skeleton-Line-Based Graph Convolutional Neural Network for Areal Settlements' Shape Classification. 2022
Liu, C. et al. TriangleConv: A Deep Point Convolutional Network for Recognizing Building Shapes in Map Space. 2021
Yan, X. et al. Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps. 2021
Zhao, R. et al. Recognition of building group patterns using graph convolutional network. 2020
Yan, X. et al. A graph convolutional neural network for classification of building patterns using spatial vector data. 2019
Lee, J. et al. Machine Learning Classification of Buildings for Map Generalization. 2017

Content Description

Xu, J. et al. Map Reading and Analysis with GPT-4V(ision). 2024

Metadata Retrieval

Hu, Y. et al. Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation. 2022
Li, J. Computational Cartographic Recognition: Exploring the Use of Machine Learning and Other Computational Approaches to Map Reading. 2022
Touya, G. et al. Inferring the scale and content of a map using deep learning. 2020
Zhou, X. et al. Deep Convolutional Neural Networks for Map-Type Classification. 2018

Design Analysis

Xi, D. et al. Research on map emotional semantics using deep learning approach. 2023
Keskin, M. et al. Potential of eye-tracking for interactive geovisual exploration aided by machine learning. 2023

Similarity Search

Klasen, V. et al. How we see time – the evolution and current state of visualizations of temporal data. 2023
Guo, D. et al. DeepSSN: A deep convolutional neural network to assess spatial scene similarity. 2022
Dobesova, Z. Experiment in Finding Look-Alike European Cities Using Urban Atlas Data. 2020

Text-to-Map

Zhang, Y. et al. GeoGPT: An assistant for understanding and processing geospatial tasks. 2024
MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation. n.d.

Neural Rendering (Relief Shading)

Li, S. et al. Generation Method for Shaded Relief Based on Conditional Generative Adversarial Nets. 2022
Jenny, B. et al. Cartographic Relief Shading with Neural Networks. 2021

Style Transfer

Wu, A.N. et al. GANmapper: geographical data translation. 2022
Ye, X. et al. MasterplanGAN: Facilitating the smart rendering of urban master plans via generative adversarial networks. 2022
Christophe, S. et al. Neural map style transfer exploration with GANs. 2022
Li, Z. et al. Synthetic Map Generation to Provide Unlimited Training Data for Historical Map Text Detection. 2021
Chen, X. et al. SMAPGAN: Generative Adversarial Network-Based Semisupervised Styled Map Tile Generation Method. 2021
Bogucka, E.P. et al. Projecting emotions from artworks to maps using neural style transfer. 2019
Kang, Y. et al. Transferring multiscale map styles using generative adversarial networks. 2019
Isola, P. et al. Image-to-Image Translation with Conditional Adversarial Networks. 2017

Generalization (Lines)

Yan, X. et al. A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints. 2024
Karsznia, I. et al. Using machine learning and data enrichment in the selection of roads for small-scale maps. 2024
Courtial, A. et al. DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps. 2024
Du, J. et al. Segmentation and sampling method for complex polyline generalization based on a generative adversarial network. 2022
Courtial, A. et al. Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation. 2022
Du J. et al. An ensemble learning simplification approach based on multiple machine-learning algorithms with the fusion using of raster and vector data and a use case of coastline simplification. 2022
Yu, W. et al. Data-driven polyline simplification using a stacked autoencoder-based deep neural network. 2022
Zheng, J. et al. Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection. 2021
Courtial, A. et al. Generative adversarial networks to generalise urban areas in topographic maps. 2021
Courtial, A. et al. Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation. 2020

Generalization (Polygons)

Zhou, Z. et al. SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps. 2024
Knura, M. Learning from vector data: enhancing vector-based shape encoding and shape classification for map generalization purposes. 2024
Fu, C. et al. Keeping walls straight: data model and training set size matter for deep learning in building generalization. 2024
Yan, X. et al. A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints. 2024
Courtial, A. et al. DeepMapScaler: a workflow of deep neural networks for the generation of generalised maps. 2024
Zhou, Z. et al. Move and remove: Multi-task learning for building simplification in vector maps with a graph convolutional neural network. 2023
Zhou, Z. et al. Building simplification of vector maps using graph convolutional neural networks. 2022
Courtial, A. et al. Representing Vector Geographic Information As a Tensor for Deep Learning Based Map Generalisation. 2022
Yan, X. et al. An adaptive building simplification approach based on shape analysis and representation. 2022
Courtial, A. et al. Generative adversarial networks to generalise urban areas in topographic maps. 2021
Wu, Y. et al. Application of Deep Learning for 3D building generalization. 2019
Feng, Y. et al. Learning Cartographic Building Generalization with Deep Convolutional Neural Networks. 2019
Touya, G. et al. Is deep learning the new agent for map generalization? 2019

Abstraction

Karamatsu, T. et al. Iconify: Converting Photographs into Icons. 2020

Displacement (Labels)

Oucheikh, R. et al. A feasibility study of applying generative deep learning models for map labeling. 2024
Harrie, L. et al. Label Placement Challenges in City Wayfinding Map Production—Identification and Possible Solutions. 2022
Lan, T. et al. An ANNs-Based Method for Automated Labelling of Schematic Metro Maps. 2022
Li, Y. et al. Automatic label placement of area-features using deep learning. 2020

Georeferencing and Map Registration

Wu, S. et al. Unsupervised historical map registration by a deformation neural network. 2022
Feng, J. et al. DeepMM: Deep Learning Based Map Matching With Data Augmentation. 2022
Duan, W. et al. A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition. 2021
Duan, W. et al. Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning. 2020

Inpainting (Lines)

Fang, Z. et al. A topography-aware approach to the automatic generation of urban road networks. 2022
Yu, W. et al. Filling gaps of cartographic polylines by using an encoder–decoder model. 2022

Inpainting (Raster)

Stölzle, M. et al. Reconstructing Occluded Elevation Information in Terrain Maps With Self-Supervised Learning. 2022
Li, S. et al. Integrating topographic knowledge into deep learning for the void-filling of digital elevation models. 2022
Zhou, G. et al. Voids Filling of DEM with Multiattention Generative Adversarial Network Model. 2022
Zhang, C. et al. DEM Void Filling Based on Context Attention Generation Model. 2020
Dong, G. et al. Filling Voids in Elevation Models Using a Shadow-Constrained Convolutional Neural Network. 2020
Gavriil, K. et al. Void Filling of Digital Elevation Models With Deep Generative Models. 2019
Qiu, Z. et al. Void Filling of Digital Elevation Models with a Terrain Texture Learning Model Based on Generative Adversarial Networks. 2019

3D Reconstruction

Schnürer, R. et al. Inferring implicit 3D representations from human figures on pictorial maps. 2024

Geolocalisation

Qian, C. et al. A Coarse-to-Fine Model for Geolocating Chinese Addresses. 2020

Geographic Entity Extraction

Mao, H. et al. Mapping near-real-time power outages from social media. 2019

Object and Phenomenon Detection

Liang, Y. et al. GeoAI-enhanced community detection on spatial networks with graph deep learning. 2025
Valdez, D.B. et al. A Deep Learning Approach of Recognizing Natural Disasters on Images using Convolutional Neural Network and Transfer Learning. 2021
Feng, Y. et al. Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos. 2018

Remote Sensing

Li, W. et al. Assessment of a new GeoAI foundation model for flood inundation mapping. 2023
Li, W. et al. GeoImageNet: a multi-source natural feature benchmark dataset for GeoAI and supervised machine learning. 2023
Hsu, C.-Y. et al. Explainable GeoAI: can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection. 2023
Li, W. et al. Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection under Weak Supervision. 2021
Wang, S. et al. GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection. 2021
Hsu, C.-Y. et al. Knowledge-Driven GeoAI: Integrating Spatial Knowledge into Multi-Scale Deep Learning for Mars Crater Detection. 2021
Li, W. et al. Automated terrain feature identification from remote sensing imagery: a deep learning approach. 2020
Mubin, N.A. et al. Young and mature oil palm tree detection and counting using convolutional neural network deep learning method. 2019
Sublime, J. et al. Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami. 2019
Li, W. et al. Recognizing terrain features on terrestrial surface using a deep learning model: an example with crater detection. 2017

Cleaning and Conflation

Processing Workflows

Li, Z. et al. Autonomous GIS: the next-generation AI-powered GIS. 2023

Record Linkage (Addresses)

Li, F. et al. Multi-task deep learning model based on hierarchical relations of address elements for semantic address matching. 2022
Xu, L. et al. Deep Transfer Learning Model for Semantic Address Matching. 2022
Cheng, R. et al. A location conversion method for roads through deep learning-based semantic matching and simplified qualitative direction knowledge representation. 2021
Park, S. et al. BertLoc: duplicate location record detection in a large-scale location dataset. 2021
Chen, J. et al. Deep Contrast Learning Approach for Address Semantic Matching. 2021
Lin, Y. et al. A deep learning architecture for semantic address matching. 2020

Record Linkage (Toponyms)

Fize, J. et al. Deep Learning for Toponym Resolution: Geocoding Based on Pairs of Toponyms. 2021
Alexis, K. et al. Boosting toponym interlinking by paying attention to both machine and deep learning. 2020
Santos, R. et al. Toponym matching through deep neural networks. 2018

Data Structures

Zhang, Z. et al. An Al-based Spatial Knowledge Graph for Enhancing Spatial Data and Knowledge Search and Discovery. 2021

Wayfinding and Routing

Hei, Q. et al. Detecting dynamic visual attention in augmented reality aided navigation environment based on a multi-feature integration fully convolutional network. 2023
Liu, Z. et al. DeepGPS : Deep Learning Enhanced GPS Positioning in Urban Canyons. 2022

Recommender Systems

Pramanik, S. et al. Deep Learning Driven Venue Recommender for Event-Based Social Networks. 2020

Risk Prevention

Kim, J.-M. et al. Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis. 2022
Kang, B. et al. A deep-learning-based emergency alert system. 2016

Modeling and Simulations (Physical Geography)

Estacio, I. et al. Predicting the future through observations of the past: Concretizing the role of Geosimulation for holistic geospatial knowledge. 2024
Roy, A. et al. Using generative adversarial networks (GAN) to simulate central-place foraging trajectories. 2022

Modeling and Simulations (Human Geography)

Alastal, A.I. et al. GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case). 2022
Boulila, W. et al. A novel CNN-LSTM-based approach to predict urban expansion. 2021
Wu, Y. et al. Deep Learning-Based Super-Resolution Climate Simulator-Emulator Framework for Urban Heat Studies. 2021

Ethics

Marasinghe, R. et al. Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures. 2024
Kang, Y. et al. Artificial intelligence studies in cartography: a review and synthesis of methods, applications, and ethics. 2024

Remember, this is just a starting point. Explore these resources, search for specific topics within GeoAI, and contribute your own findings to broaden the knowledge base of this rapidly evolving field!

So far, only research works applying deep learning architectures have been considered but not any traditional machine learning algorithms.

Journals