Merge pull request #22 from raquelcarmo/master

Adding ocean-modelling-litter-philab notebook

book/_bibliography/references.bib

@@ -178,4 +178,33 @@ @misc{wooster_he_xu_lattanzio
   publisher={LSF SAF}, author={Wooster, Martin and He, Jiangping and Xu, Weidong and Lattanzio, Alessio},
   urldate = {2021-11-18},
   topic = {wildfires-sensors-fires_seviri}
-  }
+  }
+
+### ocean-modelling-litter-philab
+
+@inproceedings{Mifdal2021,
+   abstract = {Marine litter is a growing problem that has been attracting attention and raising concerns over the last years. Significant quantities of plastic can be found in the oceans due to the unfiltered discharge of waste into rivers, poor waste management, or lost fishing nets. The floating elements drift on the surface of water bodies and can be aggregated by processes, such as river plumes, windrows, oceanic fronts, or currents. In this paper, we focus on detecting big patches of floating objects that can contain plastic as well as other materials with optical Sentinel 2 data. In contrast to previous work that focuses on pixel-wise spectral responses of some bands, we employ a deep learning predictor that learns the spatial characteristics of floating objects. Along with this work, we provide a hand-labeled Sentinel 2 dataset of floating objects on the sea surface and other water bodies such as lakes together with pre-trained deep learning models. Our experiments demonstrate that harnessing the spatial patterns learned with a CNN is advantageous over pixel-wise classifications that use hand-crafted features. We further provide an analysis of the categories of floating objects that we captured while labeling the dataset and analyze the feature importance for the CNN predictions. Finally, we outline the limitations of trained CNN on several systematic failure cases that we would like to address in future work by increasing the diversity in the dataset and tackling the domain shift between regions and satellite acquisitions. The dataset introduced in this work is the first to provide public large-scale data for floating litter detection and we hope it will give more insights into developing techniques for floating litter detection and classification. Source code and data are available at https://github.com/ESA-PhiLab/floatingobjects.},
+   author = {Jamila Mifdal and Nicolas Longepe and Marc Rußwurm},
+   doi = {10.5194/isprs-annals-V-3-2021-285-2021},
+   issn = {21949050},
+   issue = {3},
+   booktitle = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
+   keywords = {CNN,Deep Learning,Floating Objects,Marine Litter Detection,Oceanography,Sentinel 2},
+   month = {6},
+   pages = {285-293},
+   publisher = {Copernicus GmbH},
+   title = {Towards detecting floating objects on a global scale with learned spatial features using sentinel 2},
+   volume = {5},
+   year = {2021},
+   topic = {ocean-modelling-litter-philab}
+}
+
+@inproceedings{Carmo2021,
+   author = {Raquel Carmo and Jamila Mifdal and Marc Rußwurm},
+   url = {https://210507-004.oceansvirtual.com/view/content/skdwP611e3583eba2b/ecf65c2aaf278557ad05c213247d67a54196c9376a0aed8f1875681f182daeed},
+   booktitle = {OCEANS 2021},
+   publisher = {OCEANS 2021},
+   title = {Detecting Macro Floating Objects on Coastal Water Bodies using Sentinel-2 Data},
+   year = {2021},
+   topic = {ocean-modelling-litter-philab}
+}

book/_toc.yml

@@ -28,6 +28,9 @@ parts:
       title: Sensors
     - file: ocean/ocean-modelling
       title: Modelling
+      sections:
+      - file: ocean/modelling/ocean-modelling-litter-philab
+        title: Detecting floating objects (ESA Phi-Lab)
   - file: polar/polar-overview
     title: Polar
     sections: