Automatic Identification of Addresses: A Systematic Literature Review

Cruz, Paula; Vanneschi, Leonardo; Paìnho, Marco; Rita, Paulo

doi:10.3390/ijgi11010011

Cited by 6 publications

(4 citation statements)

References 67 publications

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“…Cruz et al analyzed 41 papers on address matching published between 2002 and 2021 and discovered that most of the relevant studies have used deep learning methods. Among them, consistent with the above in this paper, due to the complexity of Chinese addresses, Chinese address matching accounted for half of the studies [37].…”

Section: Introductionsupporting

confidence: 87%

Deep Transfer Learning Model for Semantic Address Matching

Mao

Zhang

et al. 2022

Applied Sciences

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Address matching, which aims to match an input descriptive address with a standard address in an address database, is a key technology for achieving data spatialization. The construction of today’s smart cities depends heavily on the precise matching of Chinese addresses. Existing methods that rely on rules or text similarity struggle when dealing with nonstandard address data. Deep-learning-based methods often require extracting address semantics for embedded representation, which not only complicates the matching process, but also affects the understanding of address semantics. Inspired by deep transfer learning, we introduce an address matching approach based on a pretraining fine-tuning model to identify semantic similarities between various addresses. We first pretrain the address corpus to enable the address semantic model (abbreviated as ASM) to learn address contexts unsupervised. We then build a labelled address matching dataset using an address-specific geographical feature, allowing the matching problem to be converted into a binary classification prediction problem. Finally, we fine-tune the ASM using the address matching dataset and compare the output with several popular address matching methods. The results demonstrate that our model achieves the best performance, with precision, recall, and an F1 score above 0.98.

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Section: Introductionsupporting

confidence: 87%

Deep Transfer Learning Model for Semantic Address Matching

Mao

Zhang

et al. 2022

Applied Sciences

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“…The tools are friendly and allow the digitization of aquatic systems in rural locations. Generally, address geocoding services are used in housing georeferencing processes; however, to georeference homes in rural locations without GPS cartography, mobile devices must be used [ 15 , 16 ]. This processing option involves the purchase of GPS devices and the transfer of personnel to the field to obtain the coordinates of each home.…”

Section: Discussionmentioning

confidence: 99%

Web Geographic Information System: A Support Tool for the Study, Evaluation, and Monitoring of Foci of Malaria Transmission in Mexico

Santos-Luna

Román-Pérez

Reyes-Cabrera³

et al. 2023

IJERPH

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Malaria is currently an endemic disease in Mexico. The country joined the WHO’s E-25 initiative for the elimination of Plasmodium vivax to achieve elimination and certification within the established period. Having a Web-based information system was, therefore, deemed necessary to assist in the detection, investigation, and elimination of transmission in the foci, as well as for the timely treatment of malaria-positive cases. The “Information System for the Elimination of Malaria in Mexico” was designed, developed, and implemented with a geographic vision, which includes a Web tool to georeference homes and aquatic systems, a dashboard and an indicator evaluation card for monitoring activities, notification of probable cases, and vector control among other indicators. The implementation of the system was gradual in the seven states that are currently in the malaria elimination phase; subsequently, the system was implemented in non-transmission states. In 2020, the system implementation stage began; first, the basic data of more than 96,000 homes throughout the country were georeferenced, and then the primary data capture tools of 17 formats, 32 reports, and 2 geographic viewers were enabled for information queries. A total of 56 active foci have been identified in 406 localities as well as 71 residual foci in 320 localities. Recently, the Foci Manager was developed, which is a specific tool for the study, evaluation, and monitoring of active foci through a GIS, a dashboard, and a systematized evaluation certificate. Georeferencing tools decreased the cost of spatial data collection.

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“…The result showed that the proposed method gained a higher matching accuracy for unstructured address data, with its precision, recall, and F1 score up to 0.97. Another study [26][27][28][29][30][31] from related scholars also proposed different methodologies to deal with address-matching problems in various contexts by utilizing the most advanced technologies, such as deep learning and machine learning.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data

et al. 2022

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Validating and improving the quality of global address data are important tasks in a modern society where exchanges between countries are due to active Free Trade Agreements (FTAs) and e-commerce. Addresses may be constructed with different systems for each country; therefore, to verify and improve the quality of the address data, it is necessary to understand the address system of each country in advance. In the event of food risk, it is important to identify the administrative district from the address in order to take safety measures, such as predicting the contaminated area by tracking the distribution of food in the area. In this study, we propose a method that applies a deep learning approach to verify and improve the quality of the global address data required for imported food-safety management. The address entered by the user is classified to the administrative division levels of the relevant country and the quality of the address data is verified and improved by converting them into a standardized address. Finally, the results show that the accuracy of the model is found to be approximately 90% and the proposed method is able to verify and evaluate the overseas address data quality significantly.

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Automatic Identification of Addresses: A Systematic Literature Review

Cited by 6 publications

References 67 publications

Deep Transfer Learning Model for Semantic Address Matching

Deep Transfer Learning Model for Semantic Address Matching

Web Geographic Information System: A Support Tool for the Study, Evaluation, and Monitoring of Foci of Malaria Transmission in Mexico

Deep Learning Based Improvement in Overseas Manufacturer Address Quality Using Administrative District Data

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