PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model
Encoding geospatial location is a fundamental problem for geospatial artificial intelligence (GeoAI) research. In recent years, some methods (such as Place2Vec, Space2Vec, and Sphere2Vec) were proposed to encode geospatial point as a high-dimensional vector. However, all these geospatial location en...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000494 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825199407048425472 |
|---|---|
| author | Zugang Chen Shaohua Wang Kai Wu Guoqing Li Jing Li Jian Wang |
| author_facet | Zugang Chen Shaohua Wang Kai Wu Guoqing Li Jing Li Jian Wang |
| author_sort | Zugang Chen |
| collection | DOAJ |
| description | Encoding geospatial location is a fundamental problem for geospatial artificial intelligence (GeoAI) research. In recent years, some methods (such as Place2Vec, Space2Vec, and Sphere2Vec) were proposed to encode geospatial point as a high-dimensional vector. However, all these geospatial location encoders were designed to generate a real number vector. So, when applied to some of the brain-inspired neural networks, such as Hierarchical Temporal Memory (HTM), which required the input of a binary vector, the existing methods failed. To solve the problem, based on the research from neuroscience about place cell, we proposed a new geospatial location encoding method called PlaceField2BVec. The method used the place field model to encode a location. The place field was represented by the summation of four Gaussian functions, allowing it to be stretched or divided into multiple fields as the geospatial space expanded. Then we created an HTM and devised an experiment that simulated rats moving on tables of varying sizes. The moving trajectories were encoded by PlaceField2BVec and input to the HTM. After training, we found that the artificial neurons of HTM formed a place field similar to those of hippocampal neurons in the rat brain and the distribution patterns of the place field from the two kinds of neurons were consistent. At last, our method was compared with existing Space2BVec and Buffer2BVec in terms of location prediction accuracy and to demonstrate the robustness of the binary vector encoding methods, two brain-inspired artificial neural networks— HTM and BinaryLSTM were used. The result showed that, for HTM, in smaller geospatial space the PlaceField2BVec and Buffer2BVec had about the same accuracy on average but the highest accuracy of PlaceField2BVec is 100 %; when the geospatial space extended, our method had the highest accuracy and the average accuracy of PlaceField2BVec, Space2BVec, and Buffer2BVec is 83.9 %, 25.2 % and 69.7 % after 20 times’ training. For BinaryLSTM, PlaceField2BVec always had the highest accuracy in location prediction although the accuracy decreased as the space extended. Our research can be utilized for machine self-localization, navigation, and location-related GeoAI applications, and it also contributes to the theory of cognitive maps. |
| format | Article |
| id | doaj-art-4266c970b31f43c2aecf35181eba1238 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-4266c970b31f43c2aecf35181eba12382025-02-08T05:00:00ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104402PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory modelZugang Chen0Shaohua Wang1Kai Wu2Guoqing Li3Jing Li4Jian Wang5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Hainan, China; Corresponding author at: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaHenan University of Science and Technology, Luoyang 471023, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaEncoding geospatial location is a fundamental problem for geospatial artificial intelligence (GeoAI) research. In recent years, some methods (such as Place2Vec, Space2Vec, and Sphere2Vec) were proposed to encode geospatial point as a high-dimensional vector. However, all these geospatial location encoders were designed to generate a real number vector. So, when applied to some of the brain-inspired neural networks, such as Hierarchical Temporal Memory (HTM), which required the input of a binary vector, the existing methods failed. To solve the problem, based on the research from neuroscience about place cell, we proposed a new geospatial location encoding method called PlaceField2BVec. The method used the place field model to encode a location. The place field was represented by the summation of four Gaussian functions, allowing it to be stretched or divided into multiple fields as the geospatial space expanded. Then we created an HTM and devised an experiment that simulated rats moving on tables of varying sizes. The moving trajectories were encoded by PlaceField2BVec and input to the HTM. After training, we found that the artificial neurons of HTM formed a place field similar to those of hippocampal neurons in the rat brain and the distribution patterns of the place field from the two kinds of neurons were consistent. At last, our method was compared with existing Space2BVec and Buffer2BVec in terms of location prediction accuracy and to demonstrate the robustness of the binary vector encoding methods, two brain-inspired artificial neural networks— HTM and BinaryLSTM were used. The result showed that, for HTM, in smaller geospatial space the PlaceField2BVec and Buffer2BVec had about the same accuracy on average but the highest accuracy of PlaceField2BVec is 100 %; when the geospatial space extended, our method had the highest accuracy and the average accuracy of PlaceField2BVec, Space2BVec, and Buffer2BVec is 83.9 %, 25.2 % and 69.7 % after 20 times’ training. For BinaryLSTM, PlaceField2BVec always had the highest accuracy in location prediction although the accuracy decreased as the space extended. Our research can be utilized for machine self-localization, navigation, and location-related GeoAI applications, and it also contributes to the theory of cognitive maps.http://www.sciencedirect.com/science/article/pii/S1569843225000494GeoAIGuassian functionsLocation encodingPlace fieldHierarchical Temporal MemoryCognitive map |
| spellingShingle | Zugang Chen Shaohua Wang Kai Wu Guoqing Li Jing Li Jian Wang PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model International Journal of Applied Earth Observations and Geoinformation GeoAI Guassian functions Location encoding Place field Hierarchical Temporal Memory Cognitive map |
| title | PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model |
| title_full | PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model |
| title_fullStr | PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model |
| title_full_unstemmed | PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model |
| title_short | PlaceField2BVec: A bionic geospatial location encoding method for hierarchical temporal memory model |
| title_sort | placefield2bvec a bionic geospatial location encoding method for hierarchical temporal memory model |
| topic | GeoAI Guassian functions Location encoding Place field Hierarchical Temporal Memory Cognitive map |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000494 |
| work_keys_str_mv | AT zugangchen placefield2bvecabionicgeospatiallocationencodingmethodforhierarchicaltemporalmemorymodel AT shaohuawang placefield2bvecabionicgeospatiallocationencodingmethodforhierarchicaltemporalmemorymodel AT kaiwu placefield2bvecabionicgeospatiallocationencodingmethodforhierarchicaltemporalmemorymodel AT guoqingli placefield2bvecabionicgeospatiallocationencodingmethodforhierarchicaltemporalmemorymodel AT jingli placefield2bvecabionicgeospatiallocationencodingmethodforhierarchicaltemporalmemorymodel AT jianwang placefield2bvecabionicgeospatiallocationencodingmethodforhierarchicaltemporalmemorymodel |