THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING
360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Le...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/33/2022/isprs-annals-V-4-2022-33-2022.pdf |
| _version_ | 1818204267882741760 |
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| author | B. Beaucamp T. Leduc V. Tourre M. Servières |
| author_facet | B. Beaucamp T. Leduc V. Tourre M. Servières |
| author_sort | B. Beaucamp |
| collection | DOAJ |
| description | 360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Learning approaches have been developed for this estimation task, only using flat images because these images are easier to annotate and process with standard CNNs. Thus to qualify the whole urban space, the panoramic images are divided into four flat sub-images that can be processed by the trained neural networks. The sub-images cover the 360° field of view, e.g. front, back, left, and right views. The four scores obtained are averaged to represent the level of the quality at the location of the panorama. However, this split introduces a bias since some elements of the urban space are halved over two images and the global context is lost. Based on the Place Pulse 2.0 dataset, this paper investigates the impact of splitting 360° panoramas on the perceptual scores predicted by neural networks. For each panorama, we predict the score for thirty-six overlapping sub-images. The scores were shown to have high variability and be highly dependent on the direction of the camera for the perspective images. This indicates that four images are not sufficient to capture the complexity of the perceptual qualities of the urban space. |
| first_indexed | 2024-12-12T03:38:32Z |
| format | Article |
| id | doaj.art-d15fba8c4ec341249fc0fa33695cd29c |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-12T03:38:32Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d15fba8c4ec341249fc0fa33695cd29c2022-12-22T00:39:45ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502022-05-01V-4-2022334010.5194/isprs-annals-V-4-2022-33-2022THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTINGB. Beaucamp0T. Leduc1V. Tourre2M. Servières3Nantes Université, ENSA Nantes, École Centrale Nantes, CNRS, AAU-CRENAU, UMR 1563, F-44000 Nantes, FranceNantes Université, ENSA Nantes, École Centrale Nantes, CNRS, AAU-CRENAU, UMR 1563, F-44000 Nantes, FranceNantes Université, ENSA Nantes, École Centrale Nantes, CNRS, AAU-CRENAU, UMR 1563, F-44000 Nantes, FranceNantes Université, ENSA Nantes, École Centrale Nantes, CNRS, AAU-CRENAU, UMR 1563, F-44000 Nantes, France360° imagery has been increasingly used to estimate the subjective qualities of the urban space, such as the feeling of safety or the liveliness of a place. These spherical panoramas offer an immersive view of the urban scene, close to the experience of a pedestrian. In recent years, Deep Learning approaches have been developed for this estimation task, only using flat images because these images are easier to annotate and process with standard CNNs. Thus to qualify the whole urban space, the panoramic images are divided into four flat sub-images that can be processed by the trained neural networks. The sub-images cover the 360° field of view, e.g. front, back, left, and right views. The four scores obtained are averaged to represent the level of the quality at the location of the panorama. However, this split introduces a bias since some elements of the urban space are halved over two images and the global context is lost. Based on the Place Pulse 2.0 dataset, this paper investigates the impact of splitting 360° panoramas on the perceptual scores predicted by neural networks. For each panorama, we predict the score for thirty-six overlapping sub-images. The scores were shown to have high variability and be highly dependent on the direction of the camera for the perspective images. This indicates that four images are not sufficient to capture the complexity of the perceptual qualities of the urban space.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/33/2022/isprs-annals-V-4-2022-33-2022.pdf |
| spellingShingle | B. Beaucamp T. Leduc V. Tourre M. Servières THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING |
| title_full | THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING |
| title_fullStr | THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING |
| title_full_unstemmed | THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING |
| title_short | THE WHOLE IS OTHER THAN THE SUM OF ITS PARTS: SENSIBILITY ANALYSIS OF 360° URBAN IMAGE SPLITTING |
| title_sort | whole is other than the sum of its parts sensibility analysis of 360 deg urban image splitting |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-4-2022/33/2022/isprs-annals-V-4-2022-33-2022.pdf |
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