Joni Storie

The variability of urban growth in the Mexico City conurbation reflects the complexity of changes and uncertainty experienced in many urban areas. The goal of this project is to identify how the urban expansion of the Mexico City conurbation reflects the changes in socioeconomic variables of the region over recent decades. The authors first spatially quantified the rate of urban growth from 1990 to 2020 using Landsat data and then identified the socioeconomic variables associated with this urban expansion. Results showed a progressive loss in vegetated land and an increase in urban land. The population’s access to roads and the marginalization index had the highest positive correlation with the observed urban growth. Although these variables were highly correlated with each other, access to roads was not found to be a variable of importance for projecting urban growth. Finally, two distinct zones of urban growth were determined using cluster statistics; the first showed no growth, which corresponded with more established, older municipalities closer to the city centre, and the second zone had significant growth, which corresponded to municipalities away from the centre, coinciding with urban decentralization and new lifestyle patterns. The identification of urban growth zones and socioeconomic variables associated with that growth will assist with effective planning, infrastructure development, and resource management.

Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation problem to narrow the discrepancy between satellite image datasets. As a result, image segmentation models that are then trained, could better generalize and use an existing set of labels instead of requiring new ones. This work proposes an unsupervised domain adaptation model that preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. This paper's major contribution is proposing the improved architecture of the SemI2I model, which significantly boosts the proposed model's performance and makes it competitive with the state-of-the-art CyCADA model. A second contribution is testing the CyCADA model on the remote sensing multi-band datasets such as WorldView-2 and SPOT-6. The proposed model preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. Thus, the semantic segmentation model, trained on the adapted images, shows substantial performance gain compared to the SemI2I model and reaches similar results as the state-of-the-art CyCADA model. The future development of the proposed method could include ecological domain transfer, a priori evaluation of dataset quality in terms of data distribution, or exploration of the inner architecture of the domain adaptation model.

Deep learning has become one of remote sensing scientists' most efficient computer vision tools in recent years. However, the lack of training labels for the remote sensing datasets means that scientists need to solve the domain adaptation problem to narrow the discrepancy between satellite image datasets. As a result, image segmentation models that are then trained, could better generalize and use an existing set of labels instead of requiring new ones. This work proposes an unsupervised domain adaptation model that preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. This paper's major contribution is proposing the improved architecture of the SemI2I model, which significantly boosts the proposed model's performance and makes it competitive with the state-of-the-art CyCADA model. A second contribution is testing the CyCADA model on the remote sensing multi-band datasets such as WorldView-2 and SPOT-6. The proposed model preserves semantic consistency and per-pixel quality for the images during the style-transferring phase. Thus, the semantic segmentation model, trained on the adapted images, shows substantial performance gain compared to the SemI2I model and reaches similar results as the state-of-the-art CyCADA model. The future development of the proposed method could include ecological domain transfer, {\em a priori} evaluation of dataset quality in terms of data distribution, or exploration of the inner architecture of the domain adaptation model.

The world is continuing to urbanize at an alarming rate. The ability to detect, map and monitor change within the urban system is of critical importance as urban growth happens largely at the expense of agricultural loss. Mapping land use/land cover change within urban systems using fully polarimetric synthetic aperture radar has been limited because of the complexity of scattering mechanisms within the urban system. This research examines the use of 2-component polarimetric decompositions for the identification of the urban-rural boundary. Using multi-temporal, fully polarimetric RADARSAT2 fine beam SAR imagery the Neumann 2-Component decomposition was most effective of the four decompositions tested.