Microsoft Research335 тыс
Опубликовано 9 августа 2024, 17:40
This seminar was hosted by Microsoft Research Africa, Nairobi together with the Microsoft AI for Good team in July 2024.
Knowing where people live is critical for urban planning, disaster management, and more broadly, for informing public policies. Unfortunately, in many countries, especially in the global south, census data is often outdated. At the same time, existing building and population layers are either very coarse in resolution, limited in coverage areas, or lack a temporal dimension.
In response to this need, our study leverages high-resolution PlanetScope satellite imagery and a fusion of extensive building footprint datasets to develop a robust method for estimating the extent of built-up areas globally and over time, from 2017 Q3 to 2023 Q3. Our methodology relies on an adapted version of a U-Net Convolutional Neural Network, pretrained on ImageNet, and augmented with a mixture of experts component to handle various landscapes and generalize better at a global scale. We generate precise building density values for 40 x 40-meter patches globally. Our approach enables precise and automated building density mapping to inform public decision-making at an unprecedented scale.
Speaker: Dr. Gilles Quentin Hacheme
Learn more about Microsoft Research Lab – Africa, Nairobi: microsoft.com/en-us/research/l...
Knowing where people live is critical for urban planning, disaster management, and more broadly, for informing public policies. Unfortunately, in many countries, especially in the global south, census data is often outdated. At the same time, existing building and population layers are either very coarse in resolution, limited in coverage areas, or lack a temporal dimension.
In response to this need, our study leverages high-resolution PlanetScope satellite imagery and a fusion of extensive building footprint datasets to develop a robust method for estimating the extent of built-up areas globally and over time, from 2017 Q3 to 2023 Q3. Our methodology relies on an adapted version of a U-Net Convolutional Neural Network, pretrained on ImageNet, and augmented with a mixture of experts component to handle various landscapes and generalize better at a global scale. We generate precise building density values for 40 x 40-meter patches globally. Our approach enables precise and automated building density mapping to inform public decision-making at an unprecedented scale.
Speaker: Dr. Gilles Quentin Hacheme
Learn more about Microsoft Research Lab – Africa, Nairobi: microsoft.com/en-us/research/l...
Свежие видео
Caseware and AWS: Transforming Audit and Accounting with AI and Amazon Bedrock | Amazon Web Services