Andere Kunden interessierten sich auch für
![DeepCOPD: COPD DETECTION USING DEEP LEARNING]( "DeepCOPD: COPD DETECTION USING DEEP LEARNING")
DeepCOPD: COPD DETECTION USING DEEP LEARNING29,99 €![HUMAN ACTIVITY RECOGNITION USING DEEP LEARNING]( "HUMAN ACTIVITY RECOGNITION USING DEEP LEARNING")
HUMAN ACTIVITY RECOGNITION USING DEEP LEARNING44,99 €![Trends in Deep Learning Methodologies]( "Trends in Deep Learning Methodologies")
Trends in Deep Learning Methodologies97,99 €![Semantische Segmentierung mit Deep Learning]( "Semantische Segmentierung mit Deep Learning")
Semantische Segmentierung mit Deep Learning43,90 €![Ear and Tongue Multi Biometric Identification System Using CNN]( "Ear and Tongue Multi Biometric Identification System Using CNN")
Ear and Tongue Multi Biometric Identification System Using CNN51,99 €![DeepCOPD: COPD-Erkennung mittels Deep Learning]( "DeepCOPD: COPD-Erkennung mittels Deep Learning")
DeepCOPD: COPD-Erkennung mittels Deep Learning43,90 €![Deep Learning for Biometrics]( "Deep Learning for Biometrics")
Deep Learning for Biometrics125,99 €
This research addresses multifaceted environmental challenges linked to urbanization by introducing a transferable UNET model designed for precise greenery detection in high-resolution satellite imagery. The dataset, consisting of 72 images captured over Dubai by MBRSC satellites and annotated by the Roia Foundation, serves as the foundation of our study. While rooted in Dubai, the model's adaptability extends beyond regional confines, offering a versatile solution to a spectrum of global urban environmental challenges. In addition to accurate greenery detection, the UNET model provides a holistic approach to urban land cover classification.