Polyline simplification based on the artificial neural network with constraints of generalization knowledge
Bibliographic Data
| ID | 21737666 |
|---|---|
| Authors | Jiawei Du (0000-0003-4734-9798, PLA Information Engineering University), Fang Wu (0000-0002-8436-7293, PLA Information Engineering University, corresponding author), Jichong Yin (0000-0001-9690-6373, PLA Information Engineering University), Chengyi Liu (0000-0002-6642-8785, PLA Information Engineering University), Xianyong Gong (0000-0002-6184-1300, PLA Information Engineering University) |
| Year | 2022 |
| Volume | 49 |
| Issue | 4 |
| Pages | 313-337 |
| Publication date | 2022-07-04 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Cartography and Geographic Information Science (JOURNAL) |
| Journal identifiers | ISSN: 1523-0406 • E-ISSN: 1545-0465 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/15230406.2021.2013944 |
| OpenAlex | W4206697580 |
| Language | EN |
| Citations received | 9 |
| References cited | 35 |
The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles
Artificial neural network · Data mining · Generalization · 3D Surveying and Cultural Heritage · Computer Science · Cultural Heritage Materials Analysis · Infrastructure Maintenance and Monitoring · Mathematics · Artificial Intelligence
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| Citations per year | 3 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |