English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The satellite imagery classification task is fundamental to spatial knowledgediscovery. Several image classification methods are used to create standardizedLand use and Land cover (LULC) maps, which facilitate research on spatial andecological processes and human activities. Local Climate Zones (LCZ)classification maps are an example of standardized maps which have been widelyused to demarcate the homogeneity in built and natural character in the cities.The LCZ classification scheme is primarily focused on urban climate-relatedresearch, in which 17 climate zones are mapped in a city area with the 100-150mspatial resolution. Each zone exhibits physical properties related to urbanform and functions essential for thermal behavior studies. Extending thiswidely adopted approach to create LULC maps at finer resolution using the LCZmapping scheme would benefit the allied domains of urban planning,transportation, and water resources management. This study proposes a novelsolution to generate classification maps with a 10-band Sentinel-2B dataset andConvolutional Neural Networks (CNN) at the 10m spatial resolution. Theclassification benefits from CNNs property to preserve local structures in theimage datasets. The proposed CNN model outperforms traditional machine learningmodels such as Artificial Neural Network, Random Forests, and Support VectorMachines. The overall accuracy and kappa statistic of the CNN model trained on14 urban and natural classes are 82 percent and 0.81, respectively. The studyalso discusses the utility of the model for specialized remote sensing taskssuch as change detection, identification of slum settlements, and mappingpervious/impervious layers in urban settlements with higher accuracy.

LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: Evidence from Mumbai