Land use identification through social network interaction

• URL: http://arxiv.org/abs/2112.06704v1
• Date: Sun, 5 Dec 2021 22:55:57 GMT
• Title: Land use identification through social network interaction
• Authors: Diana C. Pauca-Quispe, Cinthya Butron-Revilla, Ernesto Suarez-Lopez, Karla Aranibar-Tila, Jesus S. Aguilar-Ruiz
• Abstract summary: This research proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices and unbuilt land.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Although social network data has numerous semantic adulterations, and is not intended to be a source of geo-spatial information, in the text of posts we find pieces of important information about how people relate to their environment, which can be used to identify interesting aspects of how human beings interact with portions of land based on their activities. This research proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. It will be approached by identifying keywords with linguistic patterns from the text, and the geographical coordinates associated with the publication. Context-specific innovations are introduced to deal with data across South America and, in particular, in the city of Arequipa, Peru. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices and unbuilt land. Within the framework of urban planning and sustainable urban management, the methodology contributes to the optimization of the identification techniques applied for the updating of land use cadastres, since the results achieved an accuracy of about 90%, which motivates its application in the real context. In addition, it would allow the identification of land use categories at a more detailed level, in situations such as a complex/mixed distribution building based on the amount of data collected. Finally, the methodology makes land use information available in a more up-to-date fashion and, above all, avoids the high economic cost of the non-automatic production of land use maps for cities, mostly in developing countries.

Related papers

• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking [61.60169764507917]
  Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. We propose an innovative framework, namely Geo-Encoder, to more effectively integrate Chinese geographical semantics into re-ranking pipelines.
  arXiv  Detail & Related papers  (2023-09-04T13:44:50Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• A Coarse-to-Fine Approach for Urban Land Use Mapping Based on Multisource Geospatial Data [4.2968261363970095]
  We propose a machine learning-based approach for parcel-level urban land use mapping. We first divide the city into built-up and non-built-up regions based on parcels generated from road networks. We then adopt different classification strategies for parcels in different regions, and finally combine the classified results into an integrated land use map.
  arXiv  Detail & Related papers  (2022-08-18T13:30:56Z)
• A Visual Analytics System for Profiling Urban Land Use Evolution [5.053505466956614]
  Urban Chronicles is an open-source web-based visual analytics system that enables interactive exploration of changes in land use patterns. We show the capabilities of the system by exploring the data over several years at different scales.
  arXiv  Detail & Related papers  (2021-12-12T02:36:54Z)
• Empowering Local Communities Using Artificial Intelligence [70.17085406202368]
  It has become an important topic to explore the impact of AI on society from a people-centered perspective. Previous works in citizen science have identified methods of using AI to engage the public in research. This article discusses the challenges of applying AI in Community Citizen Science.
  arXiv  Detail & Related papers  (2021-10-05T12:51:11Z)
• GANmapper: geographical content filling [0.0]
  We present a new method to create spatial data using a generative adversarial network (GAN) Our contribution uses coarse and widely available geospatial data to create maps of less available features at the finer scale in the built environment. We employ land use data and road networks as input to generate building footprints, and conduct experiments in 9 cities around the world.
  arXiv  Detail & Related papers  (2021-08-07T05:50:54Z)
• Geolocation differences of language use in urban areas [0.0]
  We explore the use of Twitter data with precise geolocation information to resolve spatial variations in language use on an urban scale down to single city blocks. Our work shows that analysis of small-scale variations can provide unique information on correlations between language use and social context.
  arXiv  Detail & Related papers  (2021-08-01T19:55:45Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Road Mapping in Low Data Environments with OpenStreetMap [0.3162999570707049]
  A comprehensive, up-to-date mapping of the geographical distribution of roads has the potential to act as an indicator for broader economic development. This work investigates the viability of high resolution satellite imagery and crowd-sourced resources like OpenStreetMap in the construction of such a mapping.
  arXiv  Detail & Related papers  (2020-06-14T19:39:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.