Português
This project aims to explore new research problems that can be solved by using image and video data analysis. This project will be developed in collaboration with the Lisbon City Council (CML) and addresses challenges that CML launched for the scientific community. The area topic to be addressed is computer vision for knowledge extraction based on aerial and street-level city imagery. We propose a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks. Considering the challenges launched by CML and the already established collaboration between Iscte and CML, this project proposes to explore solutions to a set of urban classification problems by using video and image analysis. In this topic the goal is to identify features of the city shape, namely: i) to estimate the existence of unregistered greenspaces, as green rooftops (since CML is not currently aware of most green rooftops existing in Lisbon) and green back yards (not registered in the city council); ii) to estimate the height and deployment shape and area of Lisbon’s buildings, which will enable to develop an automatic and constantly updated tri-dimensional urban fabric map for Lisbon; iii) to estimate and automatically classify the conservation state of buildings’ facades; iv) among others. Such a methodology will be initially based on the set of aerial and street-level images obtained from CML (during the period of the scholarship). We are also considering exploiting existing city plans freely available at OpenStreetMaps to boost the convolutional neural network's training process, as well as to improve its inference performance. In a second phase, we will explore the use of other types of images such as from google street viewer and the ones directly collected by cameras in drones or city council vehicles (e.g., garbage trucks). For this second phase, we envision submitting a proposal to competitive funding and propose this topic as a master dissertation. The proposed project is multidisciplinary, inter-group (SSE and DLS), and aligns with ISTAR core lines of Smart Cities and Digital Transformation.
| Research Centre | Research Group | Role in Project | Begin Date | End Date |
|---|---|---|---|---|
| ISTAR-Iscte | Software Systems Engineering | Partner | 2021-09-01 | 2022-02-28 |
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| Name | Affiliation | Role in Project | Begin Date | End Date |
|---|---|---|---|---|
| Joao C Ferreira or Joao Ferreira | Professor Auxiliar (com Agregação) (DTDA); Integrated Researcher (ISTAR-Iscte); | Global Coordinator | 2021-09-01 | 2022-02-28 |
| Sara Eloy | Associate Researcher (ISTAR-Iscte); | Researcher | 2021-09-01 | 2022-02-28 |
| Tiago Fonseca | -- | Researcher | 2021-09-01 | 2022-02-28 |
| Tomás Gomes Silva Serpa Brandão | Professor Auxiliar (DCTI); Integrated Researcher (ISTAR-Iscte); | Researcher | 2021-09-01 | 2022-02-28 |
| Reference/Code | Funding DOI | Funding Type | Funding Program | Funding Amount (Global) | Funding Amount (Local) | Begin Date | End Date |
|---|---|---|---|---|---|---|---|
| UIDP/04466/2020 | -- | Contract | FCT - Financiamento Plurianual de Unidades de ID - Portugal | 5900 | 5900 | 2021-09-01 | 2022-02-28 |
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With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific projects with the Sustainable Development Goals is now available in Ciência_Iscte. These are the Sustainable Development Goals identified for this project. For more detailed information on the Sustainable Development Goals, click here.
