remove references without DOIs and replace coastseg reference with DO…

…I version

paper/paper.bib

@@ -224,14 +224,6 @@ @article{scheffler2017arosics
 }
 
 
-@misc{planet,
-  author =    {{Planet Labs}},
-  organization = {Planet Labs},
-  title =     {{Planet Application Program Interface: In Space for Life on Earth}},
-  year =      {2018},
-  url = {https://www.planet.com/}
-}
-
 @dataset{buscombe_2023_8187949,
   author       = {Buscombe, D. and
                   Fitzpatrick, S.},
@@ -268,6 +260,16 @@ @misc{voscoastsat
   url={https://doi.org/10.5281/zenodo.12553179}
 }
 
+@misc{CoastSeg,
+  author       = {Fitzpatrick, S. and Buscombe,D. and Lundine, M. and Warrick,J. and Vos, K.},
+  title        = {SatelliteShorelines/CoastSeg: v1.2.9},
+  year         = 2024,
+  publisher    = {Zenodo},
+  doi          = {10.5281/zenodo.12555413},
+  url          = {https://doi.org/10.5281/zenodo.12555413},
+  note         = {\url{https://github.com/SatelliteShorelines/CoastSeg/tree/v1.2.9}}
+}
+
 @misc{krause2021dea,
   title={{Digital Earth Australia notebooks and tools repository}},
   author={Krause, C. and Dunn, B. and Bishop-Taylor, R. and Adams, C. and Burton, C. and Alger, M. and Chua, S. and Phillips, C. and Newey, V. and Kouzoubov, K. and Leith, A. and Ayers, D. and Hicks, A.},
@@ -327,21 +329,6 @@ @article{bishop2021mapping
   url={https://doi.org/10.1016/j.rse.2021.112734}
 }
 
-@misc{modelzoo,
-  title={{PyTorch Model Zoo}},
-  author={PyTorch},
-  year={2020},
-  howpublished = {\url{https://pytorch.org/serve/model_zoo.html}},
-}
-
-@misc{modelzoo2,
-  title={{NVIDIA Model Zoo}},
-  author={NVIDIA},
-  year={2023},
-  howpublished = {\url{https://docs.nvidia.com/tao/tao-toolkit/text/model_zoo/overview.html
-}},
-}
-
 @article{garcia2015evaluating,
   title={Evaluating shoreline identification using optical satellite images},
   author={Garcia-Rubio, G. and Huntley, D. and Russell, P.},

paper/paper.md

@@ -39,7 +39,7 @@ bibliography: paper.bib
 
 # Summary
 
-`CoastSeg` is an interactive browser-based program that aims to broaden the adoption of satellite-derived shoreline (SDS) detection workflows among coastal scientists and coastal resource management practitioners. SDS is a sub-field of coastal sciences that aims to detect and post-process a time-series of shoreline locations from publicly available satellite imagery [@turner2021satellite; @vitousek2023future; @luijendijk2018state]. `CoastSeg` is a Python package installed via pip into a `conda` environment that serves as an toolkit for building custom SDS workflows. `CoastSeg` also provides full SDS workflow implementations via Jupyter notebooks and Python scripts that call functions and classes in the core `CoastSeg` toolkit for specific workflows. CoastSeg provides two fully functioning SDS workflows and its design allows for collaborators in the SDS software community to contribute additional workflows. All the codes, notebooks, scripts, and documentation are hosted on the `CoastSeg` GitHub repository.
+`CoastSeg` is an interactive browser-based program that aims to broaden the adoption of satellite-derived shoreline (SDS) detection workflows among coastal scientists and coastal resource management practitioners. SDS is a sub-field of coastal sciences that aims to detect and post-process a time-series of shoreline locations from publicly available satellite imagery [@turner2021satellite; @vitousek2023future; @luijendijk2018state]. `CoastSeg` is a Python package installed via pip into a `conda` environment that serves as an toolkit for building custom SDS workflows. `CoastSeg` also provides full SDS workflow implementations via Jupyter notebooks and Python scripts that call functions and classes in the core `CoastSeg` toolkit for specific workflows. CoastSeg provides two fully functioning SDS workflows and its design allows for collaborators in the SDS software community to contribute additional workflows. All the codes, notebooks, scripts, and documentation are hosted on the `CoastSeg` GitHub repository [@CoastSeg].
 
 So-called `instantaneous' SDS workflows, where shorelines are extracted from each individual satellite image rather than temporal composites [@pardopascual20121; @bishop2021mapping], follow a basic recipe, namely 1) waterline estimation, where the 2D (x,y) location of the land-sea interface is determined, and 2) water-level correction, where the waterline location is mapped onto a shore-perpendicular transect, converted to a linear distance along that transect, then corrected for water level, and referenced to a particular elevation contour on the beach [@vos2019coastsat]. The resulting measurement is called a 'shoreline' and it is the location that the waterline intersects a particular elevation datum. Water level corrections typically only account for tide [@vos2019coastsat] but recently SDS workflows have incorporated both wave setup and runup correction, which are a function of the instantaneous wave field at the time of image acquisition [@konstantinou2023satellite; @vitousek2023future; @vitousek2023model; @castelle2021satellite].
 
@@ -117,15 +117,15 @@ In contrast, CoastSeg introduces a significant improvement to this process by le
 
 # Implementation of an Alternative Deep-Learning-Based SDS Workflow
 
-As we noted above, we have developed a notebook that carries out an alternative SDS workflow based on deep-learning based semantic segmentation models. The name 'CoastSeg' is derived from this functionality—using semantic segmentation models for the precise classification of coastal geomorphological features. This advanced classification refines the extraction of shoreline data from satellite imagery. To implement this custom workflow, we created a new Jupyter notebook, and added source code to the `CoastSeg` codebase. The changes ensured that the inputs and outputs were those expected by core functions in `CoastSeg` toolkit. We call this alternative workflow the `Zoo` workflow, in reference to the fact that the deep learning models implemented originate from the `Segmentation Zoo` GitHub repository, and result from the `Segmentation Gym` deep-learning based image segmentation model training package [@buscombe2022reproducible]. The name `Zoo` has become a standard for online trained ML models [@modelzoo; @modelzoo2], and the repository contains both SDS models and others. Figure 2 describes in detail how the two workflows differ. While the optimal SDS workflow adopted for waterline detection, as determined against field validation data, will be the subject of a future manuscript, it is important to note that these models have not been thoroughly tested yet. We are currently benchmarking these models across various coastal environments, with the results to be documented in a separate repository and linked to `CoastSeg` upon conclusion.
+As we noted above, we have developed a notebook that carries out an alternative SDS workflow based on deep-learning based semantic segmentation models. The name 'CoastSeg' is derived from this functionality—using semantic segmentation models for the precise classification of coastal geomorphological features. This advanced classification refines the extraction of shoreline data from satellite imagery. To implement this custom workflow, we created a new Jupyter notebook, and added source code to the `CoastSeg` codebase. The changes ensured that the inputs and outputs were those expected by core functions in `CoastSeg` toolkit. We call this alternative workflow the `Zoo` workflow, in reference to the fact that the deep learning models implemented originate from the `Segmentation Zoo` GitHub repository, and result from the `Segmentation Gym` deep-learning based image segmentation model training package [@buscombe2022reproducible]. The name `Zoo` has become a standard for online trained ML models, and the repository contains both SDS models and others. Figure 2 describes in detail how the two workflows differ. While the optimal SDS workflow adopted for waterline detection, as determined against field validation data, will be the subject of a future manuscript, it is important to note that these models have not been thoroughly tested yet. We are currently benchmarking these models across various coastal environments, with the results to be documented in a separate repository and linked to `CoastSeg` upon conclusion.
 
 <!-- --------------------------------------- -->
 
 # Project Roadmap
 
 We intend `CoastSeg` to be a collaborative research project and encourage contributions from the SDS community. As well as implementing alternative SDS waterline detection workflows, other improvements that could continue to be made include more (or more refined) outlier detection methods, image filtering procedures, and other basic image pre- or post-processing routines, especially image restoration on degraded imagery [@vitousek2023future]. Such additions would all be possible without major changes to the existing `CoastSeg` toolkit.
 
-Integration of new models for the deep-learning workflow are planned, based on Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) spectral indices, as is a new `CoastSeg` toolbox extension for daily 3-m Planetscope imagery [@doherty2022python] from Planet Labs [@planet]. Docker may be adopted in the future for managing dependencies in the `conda` virtual environment required to run the program. Other sources of imagery and other spectral indices may have value in SDS workflows, and we encourage SDS users to contribute their advances through a `CoastSeg` Jupyter notebook implementation.
+Integration of new models for the deep-learning workflow are planned, based on Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) spectral indices, as is a new `CoastSeg` toolbox extension for daily 3-m Planetscope imagery [@doherty2022python] from Planet Labs. Docker may be adopted in the future for managing dependencies in the `conda` virtual environment required to run the program. Other sources of imagery and other spectral indices may have value in SDS workflows, and we encourage SDS users to contribute their advances through a `CoastSeg` Jupyter notebook implementation.
 
 It would be also be possible to incorporate automated satellite image subpixel co-registration in `CoastSeg` using the AROSICS package [@scheffler2017arosics]. This would co-register all available imagery to the nearest-in-time LandSat image. Further, future work could include accounting for the contributions of runup and setup to total water level [@vitousek2023model; @vos2023benchmarking]. In practice, this would merely add/subtract a height from the instantaneous predicted tide, then apply horizontal correction. However, the specific methods used to estimate runup or setup from the prevailing wave field would require integration with observed or hindcasted databases of wave conditions.