From 0c2ca0d7fc9aefdea313d6b70bb0fe89dc3e8472 Mon Sep 17 00:00:00 2001 From: Florent Guiotte Date: Tue, 14 Feb 2023 17:15:30 +0200 Subject: [PATCH] Remove code linki --- README.md | 3 ++- _bibliography/papers.bib | 1 - 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3f628ed..9f3658a 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,8 @@ bundle exec jekyll serve --livereload ## Deploy ```bash -scp -r _site/* k0v1@guiotte.fr:/home/k0v1/docker/florent/data +bundle exec jekyll build +rsync -aP --delete -n _site/ k0v1@guiotte.fr:/home/k0v1/docker/florent/data ``` ## Bibliography diff --git a/_bibliography/papers.bib b/_bibliography/papers.bib index abd0f22..5a233bd 100644 --- a/_bibliography/papers.bib +++ b/_bibliography/papers.bib @@ -186,7 +186,6 @@ doi = {10.1109/JSTARS.2022.3182030}, abstract = {Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This might be due to the lack of the dedicated large-scale annotated dataset and the data-structure discrepancy between point clouds and DTMs. To promote data-driven DTM extraction, this article collects from open sources a large-scale dataset of ALS point clouds and corresponding DTMs with various urban, forested, and mountainous scenes. A baseline method is proposed as the first attempt to train a deep neural network to extract DTMs directly from ALS point clouds via rasterization techniques, coined DeepTerRa. Extensive studies with well-established methods are performed to benchmark the dataset and analyze the challenges in learning to extract DTM from point clouds. The experimental results show the interest of the agnostic data-driven approach, with submetric error level compared to methods designed for DTM extraction. The data and source code are available online at https://lhoangan.github.io/deepterra/ for reproducibility and further similar research.}, archiveprefix = {arXiv}, - code = {https://github.com/lhoangan/6P}, eventtitle = {{{IEEE Journal}} of {{Selected Topics}} in {{Applied Earth Observations}} and {{Remote Sensing}}}, hal_id = {hal-03717178}, pdf = {https://arxiv.org/pdf/2206.03778.pdf},