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 €![Semantische Segmentierung mit Deep Learning]( "Semantische Segmentierung mit Deep Learning")
Semantische Segmentierung mit Deep Learning43,90 €![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 €![Riconoscimento dell'attività umana utilizzando il deep learning]( "Riconoscimento dell'attività umana utilizzando il deep learning")
Riconoscimento dell'attività umana utilizzando il deep learning44,99 €![Erkennung menschlicher Aktivitäten mithilfe von Deep Learning]( "Erkennung menschlicher Aktivitäten mithilfe von Deep Learning")
Erkennung menschlicher Aktivitäten mithilfe von Deep Learning68,90 €
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.