Aleksandra Świetlicka
Biographic Data
| ID | 8816388 |
|---|---|
| NAME | Aleksandra Świetlicka |
| GIVEN NAMES | Aleksandra |
| FAMILY NAME | Świetlicka |
| SIGNATURE | ŚWIETLICKA A |
| AFFILIATIONS | Poznań University of Technology |
| ORCID | 0000-0002-2576-7625 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Classification of Spatial Objects with the Use of Graph Neural Networks
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topolog…
A machine learning approach for integration of spatial development plans based on natural language processing
Spatial development plans are the basic tool for shaping spatial policy and have an impact on the implementation of the concept of sustainable development. Monitoring the implementation of plans can be difficult where no standard of plans exists that allows for obtaining comprehensive information on the arrangements of the plans, including future land development. The purpose of the research is to integrate spatial development plans by analyzing …
No prominent works on this page.
A machine learning approach for integration of spatial development plans based on natural language processing
Spatial development plans are the basic tool for shaping spatial policy and have an impact on the implementation of the concept of sustainable development. Monitoring the implementation of plans can be difficult where no standard of plans exists that allows for obtaining comprehensive information on the arrangements of the plans, including future land development. The purpose of the research is to integrate spatial development plans by analyzing …
Classification of Spatial Objects with the Use of Graph Neural Networks
Classification is one of the most-common machine learning tasks. In the field of GIS, deep-neural-network-based classification algorithms are mainly used in the field of remote sensing, for example for image classification. In the case of spatial data in the form of polygons or lines, the representation of the data in the form of a graph enables the use of graph neural networks (GNNs) to classify spatial objects, taking into account their topolog…
Artificial Intelligence (2 works) · Computer Science (2 works) · Data mining (2 works) · Geographic Information Systems Studies (2 works) · Geography (2 works) · Machine learning (2 works) · Spatial analysis (2 works) · Adjacency list (1 work) · Adjacency matrix (1 work) · Advanced Graph Neural Networks (1 work)