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Rodolfo G. Lotte

Researcher at National Institute for Space Research

Publications -  7
Citations -  155

Rodolfo G. Lotte is an academic researcher from National Institute for Space Research. The author has contributed to research in topics: Image segmentation & Contextual image classification. The author has an hindex of 3, co-authored 7 publications receiving 72 citations.

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Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images

Fabien Wagner, +10 more
- 01 Dec 2019 - 
TL;DR: In this article, a U-net convolutional network was used to identify and segment natural forests and eucalyptus plantations, and an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView-3 satellite in the Brazilian Atlantic rainforest region.
Journal ArticleDOI

3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion

Rodolfo G. Lotte, +4 more
- 08 Sep 2018 - 
TL;DR: This paper aims to explore and experiment with state-of-the-art technologies to semantically label 3D urban models over complex scenarios, and demonstrates that the proposed methodology is robust in complex scenarios.
Journal ArticleDOI

Accurate mapping of Brazil nut trees (Bertholletia excelsa) in Amazonian forests using WorldView-3 satellite images and convolutional neural networks

Matheus Pinheiro Ferreira, +5 more
- 01 Jul 2021 - 
TL;DR: In this paper, the authors used CNNs and very high-resolution (VHR) satellite images to map individual tree crowns (ITCs) and groves of B. excelsa in Amazonian forests.
Journal ArticleDOI

Roads centre-axis extraction in airborne sar images: an approach based on active contour model with the use of semi-automatic seeding

Rodolfo G. Lotte, +2 more
- 02 May 2013 - 
TL;DR: In this paper, a hybrid method for the extraction of roads center-axis in a synthetic aperture radar (SAR) airborne image is presented, where candidate points are fully automatically seeded using Self-Organizing Maps (SOM), followed by a pruning process based on specific metrics.

Using Convolutional Network to Identify Tree Species Related to Forest Disturbance in a Neotropical Forest with very high resolution multispectral images

Fabien Wagner, +8 more