Sentinel-2 Imagery for Mapping and Monitoring Imperviousness in Urban Areas

Kuc, G.; Chormański, Jarosław

doi:10.5194/isprs-archives-xlii-1-w2-43-2019

Cited by 34 publications

(21 citation statements)

References 2 publications

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“…Built-up surfaces have higher reflectance in band B11 according to [68]. Each image was loaded in R as a raster file, with bands as individual layers.…”

Section: Land Cover Classificationmentioning

confidence: 99%

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

2021

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Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.

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“…Built-up surfaces have higher reflectance in band B11 according to [68]. Each image was loaded in R as a raster file, with bands as individual layers.…”

Section: Land Cover Classificationmentioning

confidence: 99%

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

2021

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“…Even though S2 offers thirteen multi-spectral bands, we only made use of the Red, Green, Blue, and Near Infrared bands as they are the only ones provided at the greatest resolution of 10 m. Additionally, the Normalized Difference Vegetation Index (NDVI) is computed considering it is a reliable source of information about impervious surfaces in urban spaces [56]. Obviously, there will be exceptions such as buildings whose roofs are covered by grass.…”

Section: Data-set Generationmentioning

confidence: 99%

A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery

et al. 2021

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The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of human attention is required by these remote sensing tasks, a lot of effort has been made to automate them. However, the vast majority of the approaches rely on very high-resolution satellite imagery (<2.5 m) whose costs are not yet affordable for maintaining up-to-date maps. Working with the limited spatial resolution provided by high-resolution satellite imagery such as Sentinel-1 and Sentinel-2 (10 m) makes it hard to detect buildings and roads, since these labels may coexist within the same pixel. This paper focuses on this problem and presents a novel methodology capable of detecting building and roads with sub-pixel width by increasing the resolution of the output masks. This methodology consists of fusing Sentinel-1 and Sentinel-2 data (at 10 m) together with OpenStreetMap to train deep learning models for building and road detection at 2.5 m. This becomes possible thanks to the usage of OpenStreetMap vector data, which can be rasterized to any desired resolution. Accordingly, a few simple yet effective modifications of the U-Net architecture are proposed to not only semantically segment the input image, but also to learn how to enhance the resolution of the output masks. As a result, generated mappings quadruplicate the input spatial resolution, closing the gap between satellite and aerial imagery for building and road detection. To properly evaluate the generalization capabilities of the proposed methodology, a data-set composed of 44 cities across the Spanish territory have been considered and divided into training and testing cities. Both quantitative and qualitative results show that high-resolution satellite imagery can be used for sub-pixel width building and road detection following the proper methodology.

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“…The normalized difference built-up index (NDBI) highlights the urban areas with a typically higher reflectance from SWIR than the NIR [35]. The values ranged from −1 to 0.69 in Nice and from −0.59 to 0.41 in Singapore.…”

Section: Land Cover Indexmentioning

confidence: 99%

Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

Kim

Liu

Liong

et al. 2021

Water

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The digital elevation model (DEM) is crucial for various applications, such as land management and flood planning, as it reflects the actual topographic characteristic on the Earth’s surface. However, it is quite a challenge to acquire the high-quality DEM, as it is very time-consuming, costly, and often confidential. This paper explores a DEM improvement scheme using an artificial neural network (ANN) that could improve the German Aerospace’s TanDEM-X (12 m resolution). The ANN was first trained in Nice, France, with a high spatial resolution surveyed DEM (1 m) and then applied on a faraway city, Singapore, for validation. In the ANN training, Sentinel-2 and TanDEM-X data of the Nice area were used as the input data, while the ground truth observation data of Nice were used as the target data. The applicability of iTanDEM-X was finally conducted at a different site in Singapore. The trained iTanDEM-X shows a significant reduction in the root mean square error of 43.6% in Singapore. It was also found that the improvement for different land covers (e.g., vegetation and built-up areas) ranges from 20 to 65%. The paper also demonstrated the application of the trained ANN on Ho Chi Minh City, Vietnam, where the ground truth data are not available; for cases such as this, a visual comparison with Google satellite imagery was then utilized. The DEM from iTanDEM-X with 10 m resolution categorically shows much clearer land shapes (particularly the roads and buildings).

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Sentinel-2 Imagery for Mapping and Monitoring Imperviousness in Urban Areas

Cited by 34 publications

References 2 publications

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery

Satellite DEM Improvement Using Multispectral Imagery and an Artificial Neural Network

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