From 9980b9ccd3ea953074e2b6a97347577842208c55 Mon Sep 17 00:00:00 2001 From: Yonatan Tarazona Date: Tue, 15 Oct 2024 15:51:11 -0500 Subject: [PATCH 1/2] adding citation --- README.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index c646803..04a067c 100644 --- a/README.md +++ b/README.md @@ -11,11 +11,18 @@ [![tests](https://github.com/yotarazona/scikit-eo/actions/workflows/tests.yml/badge.svg)](https://github.com/yotarazona/scikit-eo/actions/workflows/tests.yml) - - + +# Journal of Open Source Software + +## Citation + +Please, to cite the ```scikit-eo``` package in publications, use this paper: + +Tarazona, Y., Benitez-Paez, F., Nowosad, J., Drenkhan, F., Nowosad, J., and Timaná, M. (2024). **scikit-eo: A Python package for Remote Sensing Data Analysis**. *Journal of Open Source Software*, 9(99), 6692. DOI: [10.21105/joss.06692](https://joss.theoj.org/papers/10.21105/joss.06692) + # Introduction Nowadays, remotely sensed data has increased dramatically. Microwaves and optical images with different spatial and temporal resolutions are available and are used to monitor a variety of environmental issues such as deforestation, land degradation, land use and land cover change, among others. Although there are efforts (i.e., Python packages, forums, communities, etc.) to make available line-of-code tools for pre-processing, processing and analysis of satellite imagery, there is still a gap that needs to be filled. In other words, too much time is still spent by many users developing Python lines of code. Algorithms for mapping land degradation through a linear trend of vegetation indices, fusion optical and radar images to classify vegetation cover, and calibration of machine learning algorithms, among others, are not available yet. From dd519529825244c6a9bee235cfd49753917a1bc8 Mon Sep 17 00:00:00 2001 From: Yonatan Tarazona Date: Tue, 15 Oct 2024 15:51:57 -0500 Subject: [PATCH 2/2] adding citation + --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 04a067c..ec6c67a 100644 --- a/README.md +++ b/README.md @@ -13,8 +13,6 @@ - - # Journal of Open Source Software ## Citation @@ -23,6 +21,8 @@ Please, to cite the ```scikit-eo``` package in publications, use this paper: Tarazona, Y., Benitez-Paez, F., Nowosad, J., Drenkhan, F., Nowosad, J., and Timaná, M. (2024). **scikit-eo: A Python package for Remote Sensing Data Analysis**. *Journal of Open Source Software*, 9(99), 6692. DOI: [10.21105/joss.06692](https://joss.theoj.org/papers/10.21105/joss.06692) + + # Introduction Nowadays, remotely sensed data has increased dramatically. Microwaves and optical images with different spatial and temporal resolutions are available and are used to monitor a variety of environmental issues such as deforestation, land degradation, land use and land cover change, among others. Although there are efforts (i.e., Python packages, forums, communities, etc.) to make available line-of-code tools for pre-processing, processing and analysis of satellite imagery, there is still a gap that needs to be filled. In other words, too much time is still spent by many users developing Python lines of code. Algorithms for mapping land degradation through a linear trend of vegetation indices, fusion optical and radar images to classify vegetation cover, and calibration of machine learning algorithms, among others, are not available yet.