2019-Multimedia-Satellite-Task

What is this task about?

The main objective of the Multimedia Satellite Task is to extract complementary information associated with events which are present in Satellite Imagery and Social Media. Due to their high socio-economic impact, we built upon the last two year's of Multimedia Satellite and continue to focus on flooding events. The task moves forward the state-of-the-art in flood impact assessment by concentrating on aspects that are important but are not generally studied by multimedia researchers. The main objective of this year's task is to quantify severity of flooding events from news articles and satellite imagery. The task involves the following three subtasks:

News Image Topic Disambiguation

Participants receive links to a set of images that appeared in online news articles (English). They are asked to build a binary image classifier that predicts whether or not the topic of the article in which each image appeared was a water-related natural-disaster event. All of the news articles in the data set contain a flood-related keyword, e.g., ´´flood'', but their topics are ambiguous. For example, a news article might mention a ´´flood of flowers'', without being an article on the topic of a natural-disaster flooding event.

Figure 1. Sample images extracted from articles of our dataset. The goal of this task is to classify in two classes whether they belong to a flooding event or not.

Multimodal Flood Level Estimation

In the second subtask, participants receive a set of links to online news articles (English) and the accompanying images. The set has been filtered to include only news articles for which the accompanying image depicts a flooding event. Participants are asked to build a binary classifier that predicts whether or not the image contains at least one person standing in water above the knee. Participants can use image-features only, but the task encourages a combination of image and text features, and even use of satellite imagery.

Figure 2. Sample flood-event related images from articles of our dataset. The goal of this task is to classify images based on text and visual information whether there are people standing in water that is above knee level.

City-centered satellite sequences

In this complementary subtask, participants receive a set of sequences of satellite images that depict a certain city over a certain length of time. They are required to create a binary classifier that determines whether or not there was a flooding event ongoing in that city at that time. Because this is the first year we work with sequences of satellite images, the data will be balanced so that the prior probability of the image sequence depicting a flooding event is 50%. This design decision will allow us to better understand the task. Challenges of the task include cloud cover, and ground-level changes with non-flood causes.

Figure 3. Sample image sequences for different cities from our dataset. The goal of this subtask is to classifiy image sequences into two classes whether they belong to a flooding event or not.

What are important dates for Task-Participation?

May 15, 2019 Development data release
May 30, 2019 Test data release
September 20, 2019 Run submission due
September 23, 2019 Results returned to the participants
September 30, 2019 Working notes paper submission deadline
October 27-29, 2019 MediaEval Workshop, Nice, France

Who are the task organizers?

Task auxiliaries

Where to get more information?

Detailed information on the task including description of the data, provided features, run submission, etc. can be found on the task wiki (Note: You require access to the repository). If you need help or have any questions please contact Benjamin Bischke (firstname.lastname at dfki.de).

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