Search Results for author: Sassan Saatchi

Found 6 papers, 1 papers with code

Estimating Canopy Height at Scale

1 code implementation3 Jun 2024 Jan Pauls, Max Zimmer, Una M. Kelly, Martin Schwartz, Sassan Saatchi, Philippe Ciais, Sebastian Pokutta, Martin Brandt, Fabian Gieseke

We propose a framework for global-scale canopy height estimation based on satellite data.

Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction

no code implementations29 Jan 2024 Fabien H Wagner, Samuel Favrichon, Ricardo Dalagnol, Mayumi CM Hirye, Adugna Mullissa, Sassan Saatchi

Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images.

Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model

no code implementations2 Jun 2023 Fabien H Wagner, Sophia Roberts, Alison L Ritz, Griffin Carter, Ricardo Dalagnol, Samuel Favrichon, Mayumi CM Hirye, Martin Brandt, Philipe Ciais, Sassan Saatchi

Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity, but it is challenging to measure accurately from the ground and from space.

Mapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021

no code implementations17 Nov 2022 Fabien H Wagner, Ricardo Dalagnol, Celso HL Silva-Junior, Griffin Carter, Alison L Ritz, Mayumi CM Hirye, Jean PHB Ometto, Sassan Saatchi

Here, we map tropical tree cover and deforestation between 2015 and 2022 using 5 m spatial resolution Planet NICFI satellite images over the state of Mato Grosso (MT) in Brazil and a U-net deep learning model.

K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation

no code implementations18 May 2022 Fabien H. Wagner, Ricardo Dalagnol, Alber H. Sánchez, Mayumi C. M. Hirye, Samuel Favrichon, Jake H. Lee, Steffen Mauceri, Yan Yang, Sassan Saatchi

The model detects $k$ hard clustering classes represented in the model as $k$ discrete binary masks and their associated $k$ independently generated textures, that combined are a simulation of the original image.

Clustering Image Segmentation +1

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