Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping
Abstract
:1. Introduction
2. Previous Work
3. Basics on Transport Systems and Mobility Demand Data
3.1. Mobility Demand Spatial Characteristics
3.2. Mobility Demand Temporal Characteristics
- long-period dynamics: they result from territorial, social, and economic changes such as the variations in the gross direct product of a country, and they essentially describe the changes of a territory and of the relevant economy over the years;
- periodic dynamics: they refer to demand values that are cyclically repeated (e.g., on a seasonal or weekly basis) and point out the different users’ needs and behaviours over different times of long periods (e.g., seasons over years or days over weeks);
- daily dynamics: they are strictly correlated with the users’ daily activities, such as living and working.
- in mainly residential zones (i.e., zones with a high density of housing), the generated demand is greater than the attracted demand—i.e., —in the morning, since people leave their houses to reach workplaces, schools, or other economic activities. Conversely, attracted demand is greater than generated demand—i.e., —in the afternoon and in the evening, when most people return home. During the other periods of the day, the generated and attracted demands are similar. An example of such differences is depicted in Figure 4a, where the hourly generated and attracted demands of a mainly residential zone are represented for a whole day.
- in mainly working areas (i.e., zones with a high density of workplaces), the attracted demand is greater than the generated demand—i.e., —in the morning when most of the people go to work, while the generated demand is greater than the attracted demand—i.e., —in the afternoon and in the evening, when most of the people return to home. During the other periods of the day, the generated and attracted demands are similar. An example of such differences is depicted in Figure 4b, where the hourly generated and attracted demands of a mainly working zone are shown for a whole day.
- in mixed zones, there is not a clear difference between the generated and attracted demands, i.e., is overall comparable to at all hours of the day, as shown in the example of Figure 4c.
3.3. Mobility Demand Modal Split
4. Materials and Methods
4.1. Case Study
4.2. Multimodal Decision Fusion of Remote Sensing and Mobility Demand Data
- In the case of a pixel located within the OD zones (i.e., ) and of an urban land-use class (i.e., ), plugging Assumptions (6a) and (6b) in (5) yields:In this case, the pixelwise posterior of each urban land-use class is decomposed as the product of two terms, associated with the probability of the urban cover as a whole, given the remote sensing observations, and with the probability of the urban land-use class, given the transport features and the membership to the urban cover.
- In the case of a land-cover class () and of an arbitrary pixel (), Assumption (6c) implies .
- In the case of a pixel located outside the OD zones (i.e., ), only remote sensing features are available (); therefore, again.
- (a)
- a ground truth map regarding the individual land-cover classes in and the urban land cover;
- (b)
- a subset of the zones belonging to each urban land-use class.
4.3. Markovian Region-Based Multimodal Fusion of Remote Sensing and Mobility Demand Data
5. Experimental Results
- (i)
- Pixelwise classification of the remotely sensed image, using its multispectral channels as features—This is meant as a consolidated baseline for land-cover mapping. Random forest was chosen as a well-known benchmark classifier and was trained to discriminate all the classes in . The training samples for “vegetation” and “water bodies” are shown in Figure 8. Regarding the urban land-use classes, pixelwise training samples were obtained through the spatial intersection between the training regions of “urban cover” in Figure 8a and the training OD zones in Figure 7a;
- (ii)
- Pixelwise classification using not only the multispectral channels but also additional features including the normalised difference vegetation index (NDVI) and texture features—Random forest has been used in this case as well, thanks to its fully nonparametric formulation that allows the application to heterogeneous input features. Texture analysis is conducted using the well-known first-order histogram (FOH) and grey-level cooccurrence matrix (GLCM) approaches [71,72]. The FOH variance and GLCM contrast and variance features were extracted from all channels of the input Sentinel-2 image. Preliminary experiments, not reported for brevity, have been performed to tune the parameters of the FOH and GLCM texture analysis algorithms to optimise the classification results. Texture features have been found informative in the literature of land-use mapping from remote sensing imagery (e.g., [73,74]), and this experiment is aimed at discussing the behaviour of the proposed methods compared to a traditional approach to land-use classification from EO data. The training set used for this experiment is the same as in (i);
- (iii)
- Soft-majority voting on the posteriors computed by classifier (i)—In this case, for each OD zone and each urban land-use class, first, the average of the pixelwise posteriors predicted by random forest in (i) is computed. Then, each pixel of the zone is assigned by applying the MAP rule with the averaged posteriors of the urban land-use classes and with the pixelwise posteriors of the nonurban land-cover classes. Averaging is applied only to the posteriors of the urban land-use classes (and not to those of “vegetation” and “water bodies”) to take into account that the zonization is associated with the urban mobility and generally not with other land covers. The aim of this experiment is to appreciate the possible contribution of the spatial discretization associated with the OD zones within a traditional classification scheme as in (i), in comparison to the developed techniques in which mobility-related information is exploited in terms both of spatial structure and of transport demand features;
- (iv)
- Soft-majority voting as in (iii), applied here to the pixelwise posteriors obtained in (ii) from input spectral channels and additional features—While the rationale of this experiment is similar to that of (iii), here, the focus is on evaluating the possible benefit of combining a traditional land-use classification strategy with the spatial structure of mobility demand data.
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Residential ■ | Mixed ■ | Working ■ | |
---|---|---|---|
Producer accuracy (%) | 87.5 | 80.0 | 100 |
User accuracy (%) | 87.5 | 85.0 | 85.0 |
Overall accuracy (%) | 85.0 | ||
Average accuracy (%) | 90.0 | ||
Cohen’s | 0.78 |
Producer Accuracy (%) | |||||
---|---|---|---|---|---|
Residential ■ | Mixed ■ | Working ■ | Vegetation ■ | Water Body ■ | |
(i) | 34.54 | 45.77 | 23.28 | 41.90 | 99.96 |
(ii) | 36.37 | 46.62 | 19.58 | 51.36 | 99.99 |
(iii) | 37.18 | 63.13 | 0 | 41.90 | 99.97 |
(iv) | 28.49 | 55.22 | 17.22 | 51.36 | 99.99 |
Proposed pixelwise | 90.42 | 87.07 | 77.58 | 42.94 | 99.99 |
Proposed MRF region-based | 70.71 | 88.69 | 99.99 | 99.23 | 100 |
User Accuracy (%) | |||||
residential ■ | mixed ■ | working ■ | vegetation ■ | water body ■ | |
(i) | 20.33 | 17.73 | 27.23 | 99.56 | 99.97 |
(ii) | 22.68 | 24.75 | 28.30 | 99.81 | 99.99 |
(iii) | 38.48 | 29.67 | 0 | 99.56 | 99.97 |
(iv) | 9.13 | 16.61 | 46.31 | 99.81 | 99.99 |
Proposed pixelwise | 39.29 | 53.96 | 77.28 | 99.55 | 100 |
Proposed MRF region-based | 88.18 | 84.68 | 76.04 | 100 | 100 |
Overall Accuracy (%) | Average Accuracy (%) | Cohen’s | |||
(i) | 50.12 | 49.09 | 0.3820 | ||
(ii) | 54.76 | 48.44 | 0.4274 | ||
(iii) | 52.82 | 50.79 | 0.4138 | ||
(iv) | 49.32 | 50.46 | 0.3856 | ||
Proposed pixelwise | 70.25 | 79.60 | 0.6286 | ||
Proposed MRF region-based | 89.98 | 91.72 | 0.8706 |
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Pastorino, M.; Gallo, F.; Di Febbraro, A.; Moser, G.; Sacco, N.; Serpico, S.B. Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping. Remote Sens. 2022, 14, 3370. https://doi.org/10.3390/rs14143370
Pastorino M, Gallo F, Di Febbraro A, Moser G, Sacco N, Serpico SB. Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping. Remote Sensing. 2022; 14(14):3370. https://doi.org/10.3390/rs14143370
Chicago/Turabian StylePastorino, Martina, Federico Gallo, Angela Di Febbraro, Gabriele Moser, Nicola Sacco, and Sebastiano B. Serpico. 2022. "Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping" Remote Sensing 14, no. 14: 3370. https://doi.org/10.3390/rs14143370