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Upscaling AI Solutions for Large Scale Mapping Applications

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 39549

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Special Issue Editors

Prof. Dr. Fabio Remondino

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Guest Editor

3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), 38123 Trento, Italy
Interests: photogrammetry; laser scanning; optical metrology; 3D; AI; quality control
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Joep Crompvoets

E-Mail Website
Guest Editor

Public Governance Institutre, KU Leuven, Parkstraat 45, Bus 3609, B-3000 Leuven, Belgium
Interests: e-governance; digital transformation; interoperability; spatial data infrastructure
Special Issues, Collections and Topics in MDPI journals

Prof. Norbert Haala

E-Mail Website
Guest Editor

Institute for Photogrammetry, University of Stuttgart, Geschwister-Scholl-Strasse 24, 70174 Stuttgart, Germany
Interests: point cloud generation and analysis; joint processing of LiDAR and image data; virtual city models

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is entering all domains, with innovative solutions and results that would have been both unpredictable and unachievable just a few years ago. In a few words, AI (including machine and deep learning methods) is the ability of computers to perform a task that typically requires some level of human intelligence. AI is bringing advantages in many fields, and everybody is talking about the adoption of AI methods to solve problems and process data.

Geospatial AI, i.e., the use of AI for the processing and understanding of large geospatial data, is also growing. Geospatial data include images and point clouds captured and generated from spaceborne and airborne sensors or mobile mapping platforms. Recently, interesting research results have been presented for point cloud classification, object detection, feature extraction, semantic segmentation, etc. Nonetheless, the challenge remains on how to apply these methods and results to nationwide 3D data sets and how to have solutions that are reliable, replicable, and applicable to National Mapping and Cadastral Agencies (NMCA) problems.

This IJGI Special Issue is organized in collaboration with EuroSDR (http://eurosdr.net/)—European Spatial Data Research, a not-for-profit organization linking NMCAs with research institutes and universities in Europe for the purpose of applied research in spatial data provision, management, and delivery.

The Special Issue aims to raise awareness and understanding of AI methods applied to large geodata as well as to collect ongoing research activities, best practices, method evaluations, and applications to boost the use of AI within the context of NMCA. The Special Issue also aims to understand the impact, benefits, effect, and open issues of AI applied to large geospatial datasets.

Topics

Authors are invited to submit papers related to:

Geo-information derived with Artificial Intelligence methods
Machine and deep learning applied to large geodata
Large-scale point cloud classification
AI-based objected detection in large satellite or aerial image blocks
Geodata processing boosted by AI
Extraction of 2D and 3D geo-information from large geospatial datasets
Benchmarks related to GeoAI
Validation of AI methods on very large geospatial data
Geospatial analytics for NMCA applications
AI Cloud computing applied to large geodata
Requirements of AI methods for NMCA needs and other relevant governmental organizations
GeoAI applications

Prof. Dr. Fabio Remondino
Prof. Dr. ir. Joep Crompvoets
Prof. Norbert Haala
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Artificial Intelligence
NMCA
geospatial
mapping
benchmark
large scale
geo-information

Published Papers (7 papers)

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Research

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27 pages, 17101 KiB

Open AccessArticle

The Influence of Point Cloud Accuracy from Image Matching on Automatic Preparation of Training Datasets for Object Detection in UAV Images

by Paulina Zachar, Wojciech Ostrowski, Anna Płatek-Żak and Zdzisław Kurczyński

ISPRS Int. J. Geo-Inf. 2022, 11(11), 565; https://doi.org/10.3390/ijgi11110565 - 10 Nov 2022

Cited by 1 | Viewed by 3002

Abstract

The dynamic development of deep learning methods in recent years has prompted the widespread application of these algorithms in the field of photogrammetry and remote sensing, especially in the areas of image recognition, classification, and object detection. Still, one of the biggest challenges in this field is the low availability of training datasets, especially regarding applications of oblique aerial imagery and UAV data. The process of acquiring such databases is labor-intensive. The solution to the problem of the unavailability of datasets and the need for manual annotation is to automate the process of generating annotations for images. One such approach is used in the following work. The proposed methodology for semi-automating the creation of training datasets was applied to detect objects on nadir and oblique images acquired from UAV. The methodology includes the following steps: (1) the generation of a dense 3D point cloud by two different methods: UAV photogrammetry and TLS (terrestrial laser scanning); (2) data processing, including clipping to objects and filtering of point clouds; (3) the projection of cloud points onto aerial images; and (4) the generation of bounding boxes bounding the objects of interest. In addition, the experiments performed are designed to test the accuracy and quality of the training datasets acquired in the proposed way. The effect of the accuracy of the point cloud extracted from dense UAV image matching on the resulting bounding boxes extracted by the proposed method was evaluated. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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25 pages, 6371 KiB

Open AccessArticle

VHRShips: An Extensive Benchmark Dataset for Scalable Deep Learning-Based Ship Detection Applications

by Serdar Kızılkaya, Ugur Alganci and Elif Sertel

ISPRS Int. J. Geo-Inf. 2022, 11(8), 445; https://doi.org/10.3390/ijgi11080445 - 10 Aug 2022

Cited by 3 | Viewed by 3775

Abstract

The classification of maritime boats and ship targets using optical satellite imagery is a challenging subject. This research introduces a unique and rich ship dataset named Very High-Resolution Ships (VHRShips) from Google Earth images, which includes diverse ship types, different ship sizes, several inshore locations, and different data acquisition conditions to improve the scalability of ship detection and mapping applications. In addition, we proposed a deep learning-based multi-stage approach for ship type classification from very high resolution satellite images to evaluate the performance of the VHRShips dataset. Our “Hierarchical Design (HieD)” approach is an end-to-end structure that allows the optimization of the Detection, Localization, Recognition, and Identification (DLRI) stages, independently. We focused on sixteen parent ship classes for the DLR stages, and specifically considered eight child classes of the navy parent class at the identification stage. We used the Xception network in the DRI stages and implemented YOLOv4 for the localization stage. Individual optimization of each stage resulted in F1 scores of 99.17%, 94.20%, 84.08%, and 82.13% for detection, recognition, localization, and identification, respectively. The end-to-end implementation of our proposed approach resulted in F1 scores of 99.17%, 93.43%, 74.00%, and 57.05% for the same order. In comparison, end-to-end YOLOv4 yielded F1-scores of 99.17%, 86.59%, 68.87%, and 56.28% for DLRI, respectively. We achieved higher performance with HieD than YOLOv4 for localization, recognition, and identification stages, indicating the usability of the VHRShips dataset in different detection and classification models. In addition, the proposed method and dataset can be used as a benchmark for further studies to apply deep learning on large-scale geodata to boost GeoAI applications in the maritime domain. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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21 pages, 9424 KiB

Open AccessArticle

The Influence of Data Density and Integration on Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in Mediterranean Oak Forests

by Vahid Nasiri, Seyed Mohammad Moein Sadeghi, Fardin Moradi, Samaneh Afshari, Azade Deljouei, Verena C. Griess, Carmen Maftei and Stelian Alexandru Borz

ISPRS Int. J. Geo-Inf. 2022, 11(8), 423; https://doi.org/10.3390/ijgi11080423 - 26 Jul 2022

Cited by 9 | Viewed by 2343

Abstract

Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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26 pages, 12318 KiB

Open AccessArticle

Global Spatial Suitability Mapping of Wind and Solar Systems Using an Explainable AI-Based Approach

by Mourtadha Sarhan Sachit, Helmi Zulhaidi Mohd Shafri, Ahmad Fikri Abdullah, Azmin Shakrine Mohd Rafie and Mohamed Barakat A. Gibril

ISPRS Int. J. Geo-Inf. 2022, 11(8), 422; https://doi.org/10.3390/ijgi11080422 - 26 Jul 2022

Cited by 8 | Viewed by 3446

Abstract

An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km²) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km²) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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17 pages, 6461 KiB

Open AccessArticle

Revising Cadastral Data on Land Boundaries Using Deep Learning in Image-Based Mapping

by Bujar Fetai, Dejan Grigillo and Anka Lisec

ISPRS Int. J. Geo-Inf. 2022, 11(5), 298; https://doi.org/10.3390/ijgi11050298 - 04 May 2022

Cited by 7 | Viewed by 3134

Abstract

One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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24 pages, 7763 KiB

Open AccessArticle

A Lightweight Object Detection Method in Aerial Images Based on Dense Feature Fusion Path Aggregation Network

by Liming Zhou, Xiaohan Rao, Yahui Li, Xianyu Zuo, Baojun Qiao and Yinghao Lin

ISPRS Int. J. Geo-Inf. 2022, 11(3), 189; https://doi.org/10.3390/ijgi11030189 - 11 Mar 2022

Cited by 14 | Viewed by 3948

Abstract

In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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Review

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44 pages, 2201 KiB

Open AccessReview

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

by Wenwen Li and Chia-Yu Hsu

ISPRS Int. J. Geo-Inf. 2022, 11(7), 385; https://doi.org/10.3390/ijgi11070385 - 11 Jul 2022

Cited by 24 | Viewed by 15731

Abstract

GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography. Although much progress has been made in exploring the integration of AI and Geography, there is yet no clear definition of GeoAI, its scope of research, or a broad discussion of how it enables new ways of problem solving across social and environmental sciences. This paper provides a comprehensive overview of GeoAI research used in large-scale image analysis, and its methodological foundation, most recent progress in geospatial applications, and comparative advantages over traditional methods. We organize this review of GeoAI research according to different kinds of image or structured data, including satellite and drone images, street views, and geo-scientific data, as well as their applications in a variety of image analysis and machine vision tasks. While different applications tend to use diverse types of data and models, we summarized six major strengths of GeoAI research, including (1) enablement of large-scale analytics; (2) automation; (3) high accuracy; (4) sensitivity in detecting subtle changes; (5) tolerance of noise in data; and (6) rapid technological advancement. As GeoAI remains a rapidly evolving field, we also describe current knowledge gaps and discuss future research directions. Full article

(This article belongs to the Special Issue Upscaling AI Solutions for Large Scale Mapping Applications)

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