Team New_folder

  • Marko Prelevikj, (backend, bitcoin, data curation)
  • Gojko Hajduković, (backend, imagery processing, visualization)
  • Boshko Koloski, ML: nlp, graph wizard + all in between (no sleep, kudos!)
  • Ferdi Jajai, UI (mockups + UX)

Short description

The project addresses the problem of oil spills with the intention of spreading its awareness. We combine multiple data sources, such as Wikipedia, Twitter, and Sinergise Satellite Imagery, to achieve this goal. All the data sources contain distinct information from which we gain statistics. We do all the data-crunching while preserving the data's integrity.

List of technologies used: python, bitcoincli, sentinel-hub-py, graphviz, sumy, spacy, nltk, and a lot of patience from our designer and improvized front-end guy (thanks Gojko!).


The backend code is developed with Python 3. To install the dependencies needed for reproduction of results run the following line.

pip3 install -r requirments.txt
pipenv install

# set your workdir as the repo root
# run the flask server:
export FLASK_APP=be/server.py
flask run

# open the index.html file directly in your browser

NOTE: There are unused endpoints in the server file.

Project organization

Initial data

The initial data of the oil spills is obtained from the Wikipedia list of oil spills. Since the table is incomplete and some key information were missing as exact coordinates. For such examples we fill the data manually as close as possible. Furthermore we propose a way for this to be evaluated with an algorithmic approach for early detection and awareness.

Spill Location Dates MinTonnes MaxTonnes Owner Coords
El Palito Refinery Venezuela, Golfo Triste 8 August 2020 2,700 2,700 Petróleos de Venezuela 10.694448;-68.201062
2020 Pointe D'Esny MV Wakashio oil spill Mauritius, Ile Aux Aigrettes and Mahebourg 25 July 2020 1,300 4,300 Wakashio Suisan Company Limited, Kagoshima, Japan -20.438119;57.744631
Trans Mountain oil spill Canada, British Columbia, Abbotsford 14 June 2020 118.5 184.87 Trans Mountain 49.064499; -122.159002
Norilsk diesel fuel spill Russia, Norilsk, Krasnoyarsk Krai 29 May 2020 17,500 17,500 Nornickel 69.379444;87.744444
Tanker truck pumping out sludge from a vessel New Zealand, Tauranga, Bay of Plenty 30 March 2020 1.7 1.7 -37.660155;176.231307

Earth Observation Hack

We use SINERGIZE's satellite imagery for detection of the oil leaks and comparison of between time stamps. For a given event, we scan the coordinates and obtain the imagery in the interval of [day-30, day+30] and obtain a collection of unique satellite images of the given oil spill. For such sequence of days the minimum number of images we obtained was 12 and the maximal number of images was 20. In the end, the images are compiled together in a gif, for convenience.

Data Mining Hack

Sentiment analysis

Since we wanted to capture the notion of how such events affect society, we proceeded by exploring social media. Particularly we focused on the Twitter data the society tweets about for a given oil spill event. /

We scraped this data manually but also we provided an approach for it to be scraped automatically (Since Twitter recently updated their policies we went for the manual approach).

After obtaining the data we extracted the sentiment and analyzed it through Ekman's 5 basic emotions. We executed this analysis with the text2emotion library.

In the following table you can inspect the sentiment we extracted for some of the spills:

Spill Angry Fear Happy Sad Surprise
Delta House floating production platform spill 0.16666666666666666 0.16666666666666666 0.041666666666666664 0.20833333333333334 0.08333333333333333
Doon Iowa derail 0.06666666666666667 0.3666666666666667 0.0 0.12166666666666666 0.11166666666666668
Guarello Island iron ore export terminal diesel spill 0.06090909090909091 0.46181818181818185 0.0 0.28 0.10545454545454547
Keystone Pipeline 2017 spill 0.15142857142857144 0.4192857142857143 0.04785714285714286 0.08 0.15714285714285717
MV Chrysanthi S bunkering spill at sea 0.08333333333333333 0.5 0.08333333333333333 0.16666666666666666 0.16666666666666666
MV Solomon Trader fuel oil spill 0.055 0.2475 0.0275 0.22000000000000003 0.44250000000000006
Norilsk diesel fuel spill 0.0 0.0 0.0 0.9 0.1
Port Erin diesel spill (source ) 0.034 0.254 0.27399999999999997 0.04 0.2
Sanchi oil tanker collision with CF Crystal 0.04125 0.26875 0.0 0.17375000000000002 0.39
SeaRose FPSO production ship spill 0.0 0.45099999999999996 0.0 0.11600000000000002 0.233
T.G. Williams Well No. 1 tank battery 0.07142857142857142 0.4714285714285714 0.14714285714285716 0.13142857142857142 0.03571428571428571
Trans Mountain oil s 0.095 0.3628571428571429 0.047142857142857146 0.04142857142857143 0.3092857142857143
Trans Mountain oil spill 0.0 0.0 0.0 0.2 0.0
True Oil pipeline 0.0 0.3325 0.0 0.4175 0.0

Graph Information

Since we live in connected world, for one to analyse the interactions and one needs a good representation. Natural representation of such interactions are graphs. From them we can analyse the flows of events and interactions of entities.

With this in mind we generated a network of persons interactions based on their appearance on articles and papers.

We used the googlesearch engine to search for links of articles and web pages containing information of oil spill events. For each oil spill event we took 30 pages and summarized them to up to 5 most representative sentences, for this we used the text-rank based extractor from the sumy library.

After obtaining between 100 and 150 sentences for each oil spill we used Named Entity Recognition (NER) tool from which we extracted the entity information.

We modeled the graph in with the following heuristic, two entities were connected if they appeared in a single sentence.

One such example of an obtained graph is the following about the Pointe D'Esny MV Wakashio oil spill:


Meta information

From the NER information we extracted Organizations, Persons and Money values connected to the task.

Bitcoin hack


Since this information is of vital character we want all of this to be secured and last forever. For this purpose, we provide we store the data in a Bitcoin ledger.

Greater good hack

We believe that such an application would provide crucial information for spreading awareness of the consequences for the ecosystem of oil spill events.

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