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Land
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Application of Artificial Intelligence and Modelling Tools in Landscape Archaeology...
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Application of Artificial Intelligence and Modelling Tools in Landscape Archaeology and Geo-Design

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Landscape Archaeology".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 3908

Special Issue Editors

Dr. David H. Bassir

E-Mail Website
Guest Editor
Laboratory IRAMAT-UMR-7065 CNRS, University of Bourgogne Franche-Comté (UTBM), 90010 Belfort, France
Interests: landscape design/landscape architecture; data computation
Dr. Philippe Dillmann

E-Mail Website1 Website2
Guest Editor
1. Laboratory IRAMAT-UMR-7065 CNRS, University of Bourgogne Franche-Comté (UTBM), 90010 Belfort, France
2. NIMBE/LAPA, CEA Saclay, CEDEX, 91191 Gif sur Yvette, France
Interests: metals; material characterization

Special Issue Information

Dear Colleagues,

This special issue contributes to underline the new progress of artificial intelligence (AI) and modelling tools in landscape archaeology and geodesign. Applications of AI technologies and methods are getting more and more recognition in all the fields of land observation and control. It can helps decision makers to automate routine tasks, gain insights through data analytics from image processing, natural risk prediction, landscape archaeology observation and restoration, water management, urban-rural interactions, urban planning and development,..etc. AI tools include all intelligent methods from optimization, surface response, big data analysis or we can combine them to make an optimal decision.

All original contributions related to the above mentioned topics are welcome. Authors, should either relate case studies to their regional/social-ecological/global context and address issues of transferability considering the usefulness for and interest of an international auditorium or should at least be based on comparative study designs.

In this Special Issue, we also invite papers focusing on, but not limited to, the following topics:

  • Smart land management
  • Multi-dimensional cadastral systems
  • Cloud computing, AI, and machine learning for smart land administration
  • BIM-driven land management systems
  • Urban–rural interactions and administration
  • Big Data Analysis and image processing of Lands
  • Landscape archaeology and risk control

Dr. David H. Bassir
Dr. Philippe Dillmann
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. Land 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 2600 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

  • smart land
  • archaeology
  • modern cadaster
  • BIM
  • technological innovations
  • artificial intelligence
  • big data analysis
  • machine learning
  • water management
  • urban-rural interactions
  • urban planning and development
  • land development
  • internet of things (Iot)

Published Papers (2 papers)

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Research

26 pages, 16666 KiB  
Article
Transfer Learning with Attributes for Improving the Landslide Spatial Prediction Performance in Sample-Scarce Area Based on Variational Autoencoder Generative Adversarial Network
by Mansheng Lin, Shuai Teng, Gongfa Chen and David Bassir
Land 2023, 12(3), 525; https://doi.org/10.3390/land12030525 - 21 Feb 2023
Cited by 1 | Viewed by 1373
Abstract
Owing to the complexity of obtaining the landslide inventory data, it is a challenge to establish a landslide spatial prediction model with limited labeled samples. This paper proposed a novel strategy, namely transfer learning with attributes (TLAs), to make good use of existing [...] Read more.
Owing to the complexity of obtaining the landslide inventory data, it is a challenge to establish a landslide spatial prediction model with limited labeled samples. This paper proposed a novel strategy, namely transfer learning with attributes (TLAs), to make good use of existing landslide inventory data, a strategy that is based on a variational autoencoder of a generative adversarial network (VAEGAN) for improving the landslide spatial prediction performance in sample-scarce areas. Different from transfer learning (TL), TLAs are pretraining the model with the data reconstructed by VAEGAN, so that the models learn in advance the landslide attributes of sample-scarce areas. Accordingly, a database containing a total of 986 landslides in three study areas with 14 landslide-influencing factors was established, and each of the three models, i.e., convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) and gated recurrent units (GRUs), was respectively selected as the feature extractor of the VAEGAN to reconstruct the data with attributes and the prediction model to generate the landslide susceptibility maps to investigate and validate the proposed TLA strategy. The experimental results showed that the TLA strategy increased the mean value of evaluators, such as the area under the receiver-operating characteristic (AUROC), F1-score, precision, recall and accuracy by about 2–7% compared with TL, results that indicated that the generated data have the attribute of specific study areas and the effectiveness of TLA strategy in sample-scare areas. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Modelling Tools in Landscape Archaeology and Geo-Design)
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18 pages, 81592 KiB  
Article
Google Street View and Machine Learning—Useful Tools for a Street-Level Remote Survey: A Case Study in Ho Chi Minh, Vietnam and Ichikawa, Japan
by Duy Thong Ta and Katsunori Furuya
Land 2022, 11(12), 2254; https://doi.org/10.3390/land11122254 - 09 Dec 2022
Cited by 1 | Viewed by 1825
Abstract
This study takes one step further to complement the application of a method for mapping informal green spaces (IGSs) using an efficient combination of open-source data with simple tools and algorithms. IGSs are unofficially recognized by the government as vegetation spaces designed for [...] Read more.
This study takes one step further to complement the application of a method for mapping informal green spaces (IGSs) using an efficient combination of open-source data with simple tools and algorithms. IGSs are unofficially recognized by the government as vegetation spaces designed for recreation, gardening, and forestry in urban areas. Due to the economic crisis, many formal green spaces such as urban parks and garden projects have been postponed, while IGSs have significant potential as green space retrofits. However, because they are small and spatially continuous and cannot be fully detected via airborne surveys, they are surveyed in small areas and neglected by government and city planners. Therefore, in this research, we combined the use of Google Street View (GSV) data with machine learning to develop a survey method that can be used to survey a wide area at once. Deeplab V3+ was used to segment the semantics based on the model created using 1000 labelled photos, with an accuracy rate of nearly 65%. Applying this method gave high accuracy in Ichikawa, Japan, with 3029 photos, and matched the results of a field survey in a previous study. In contrast, low accuracy was seen in Ho Chi Minh City, with 204 photos, where the quality of the GSV data was considerably lower. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Modelling Tools in Landscape Archaeology and Geo-Design)
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