Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types

Bhuiyan, Abul Ehsan; Witharana, Chandi; Liljedahl, A. K.

doi:10.3390/jimaging6120137

Cited by 43 publications

(38 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geographic Information System (GIS) and Remote Sensing (RS) are widely used tools to investigate LULC changes and associated LST (Jahan et al 2021;Bhuiyan et al 2020a;Hegazy and Kaloop 2015). For the advancement of LULC change application, multispectral bands play a crucial role in remote sensing optical satellite imagery studies (Sicre et al 2020;Zhou et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

et al. 2021

Self Cite

View full text Add to dashboard Cite

Urbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe.

show abstract

Section: Introductionmentioning

confidence: 99%

Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Deep learning technology has recently been widely used in VHR remote sensing image applications [43][44][45]. Lots of explorations in disaster assessments were performed in the literature [22,23,[25][26][27][28][29]42,[46][47][48][49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

2021

View full text Add to dashboard Cite

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.

show abstract

“…CNNs are particularly suitable To overcome most of the described issues, we propose the use of a convolutional neural network (CNN) allowing to consider the spatial image context through convolutional extraction of feature maps from a given input image. CNNs are particularly suitable where traditional classification methods fail and were commonly used in computer vision and particularly for semantic segmentation and object detection tasks in remote sensing [31,32], e.g., for automated calving front detection using SAR and optical satellite imagery [33][34][35], for sea-land classification in optical satellite imagery [36], for multi-temporal crop type classification in optical Landsat imagery [37], for sea ice concentration estimation in dualpolarized RADARSAT-2 imagery [38], for automated mapping of ice-wedge polygons in high-resolution satellite and unmanned aerial vehicle images [39,40], or for building and road extraction in optical and aerial satellite imagery [41,42], to only name a few. In this study, we extract supraglacial lake extents from single-polarized Sentinel-1 imagery using a modified version of a CNN originally developed for semantic segmentation of biomedical images (U-Net) [43].…”

Section: Introductionmentioning

confidence: 99%

“…In Earth Observation, encoder-decoder designs and particularly variants of U-Net are most commonly used for semantic image segmentation due to their better performance compared to other approaches including naïve-decoder models [31]. With the main aim of this study being the derivation of lake areas by means of pixel-wise classifications, object detection approaches including multi-task instance segmentation (e.g., [39,40]) were not considered or tested. In detail, we aim at the segmentation of open water lakes as well as lakes that are roughened at their surface, e.g., by wind or return higher backscatter values due to a thin ice cover and appear with fuzzy edges, low contrast and speckle noise.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning

et al. 2021

View full text Add to dashboard Cite

Supraglacial meltwater accumulation on ice sheets can be a main driver for accelerated ice discharge, mass loss, and global sea-level-rise. With further increasing surface air temperatures, meltwater-induced hydrofracturing, basal sliding, or surface thinning will cumulate and most likely trigger unprecedented ice mass loss on the Greenland and Antarctic ice sheets. While the Greenland surface hydrological network as well as its impacts on ice dynamics and mass balance has been studied in much detail, Antarctic supraglacial lakes remain understudied with a circum-Antarctic record of their spatio-temporal development entirely lacking. This study provides the first automated supraglacial lake extent mapping method using Sentinel-1 synthetic aperture radar (SAR) imagery over Antarctica and complements the developed optical Sentinel-2 supraglacial lake detection algorithm presented in our companion paper. In detail, we propose the use of a modified U-Net for semantic segmentation of supraglacial lakes in single-polarized Sentinel-1 imagery. The convolutional neural network (CNN) is implemented with residual connections for optimized performance as well as an Atrous Spatial Pyramid Pooling (ASPP) module for multiscale feature extraction. The algorithm is trained on 21,200 Sentinel-1 image patches and evaluated in ten spatially or temporally independent test acquisitions. In addition, George VI Ice Shelf is analyzed for intra-annual lake dynamics throughout austral summer 2019/2020 and a decision-level fused Sentinel-1 and Sentinel-2 maximum lake extent mapping product is presented for January 2020 revealing a more complete supraglacial lake coverage (~770 km2) than the individual single-sensor products. Classification results confirm the reliability of the proposed workflow with an average Kappa coefficient of 0.925 and a F1-score of 93.0% for the supraglacial water class across all test regions. Furthermore, the algorithm is applied in an additional test region covering supraglacial lakes on the Greenland ice sheet which further highlights the potential for spatio-temporal transferability. Future work involves the integration of more training data as well as intra-annual analyses of supraglacial lake occurrence across the whole continent and with focus on supraglacial lake development throughout a summer melt season and into Antarctic winter.

show abstract

Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types

Cited by 43 publications

References 67 publications

Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning

Contact Info

Product

Resources

About