Search Results for author: Maxim Neumann

Found 13 papers, 6 papers with code

Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing

no code implementations3 May 2025 Yuchang Jiang, Maxim Neumann

Developing accurate and reliable models for forest types mapping is critical to support efforts for halting deforestation and for biodiversity conservation (such as European Union Deforestation Regulation (EUDR)).

Benchmarking Image Segmentation +2

Heterogeneous graph neural networks for species distribution modeling

no code implementations14 Mar 2025 Lauren Harrell, Christine Kaeser-Chen, Burcu Karagol Ayan, Keith Anderson, Michelangelo Conserva, Elise Kleeman, Maxim Neumann, Matt Overlan, Melissa Chapman, Drew Purves

For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model.

Benchmarking

Planted: a dataset for planted forest identification from multi-satellite time series

no code implementations24 May 2024 Luis Miguel Pazos-Outón, Cristina Nader Vasconcelos, Anton Raichuk, Anurag Arnab, Dan Morris, Maxim Neumann

In this paper, we present a dataset consisting of data from five public satellites for recognizing forest plantations and planted tree species across the globe.

Data Augmentation Time Series

Training general representations for remote sensing using in-domain knowledge

no code implementations30 Sep 2020 Maxim Neumann, André Susano Pinto, Xiaohua Zhai, Neil Houlsby

Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples.

Representation Learning Transfer Learning

AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification

no code implementations ECCV 2020 Xiaofang Wang, Xuehan Xiong, Maxim Neumann, AJ Piergiovanni, Michael S. Ryoo, Anelia Angelova, Kris M. Kitani, Wei Hua

The discovered attention cells can be seamlessly inserted into existing backbone networks, e. g., I3D or S3D, and improve video classification accuracy by more than 2% on both Kinetics-600 and MiT datasets.

Classification General Classification +1

In-domain representation learning for remote sensing

1 code implementation15 Nov 2019 Maxim Neumann, Andre Susano Pinto, Xiaohua Zhai, Neil Houlsby

Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community.

 Ranked #1 on Multi-Label Image Classification on BigEarthNet (mAP (macro) metric)

Multi-Label Image Classification Representation Learning +1

Progressive Neural Architecture Search

18 code implementations ECCV 2018 Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.

Evolutionary Algorithms General Classification +4

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