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Multi-altitude, Multimodal Maritime Surveillance (MAMMS) Dataset

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A multimodal dataset collected using ground-based sensors, low-altitude UAVs, and satellites. It supports research on object detection, object tracking, and geolocation approximation across coastal and maritime scenarios.

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\u26a0\ufe0f Important: Dataset Access Requirement

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To download the datasets, please fill out the following User Agreement:

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Multi Altitude, Multimodal Maritime Surveillance (MAMMS) Dataset User Agreement

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You will receive a download link once the User Agreement has been registered.

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Dataset Overview

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This dataset contains data collected during simulated real-world scenarios involving people, vessels, vehicles, and artificial oil spills, captured by sensor platforms operating at different altitudes and modalities, as well as real observations in maritime environments. These sensors include:

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The dataset consists of two subsets:

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MAMMS-GL (Ground-based and Low-Altitude Sensors)

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Training data from the MAMMS-GL dataset for ground-based and low-altitude object detection and tracking.

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ScenarioSensor#images#persons#vessels#vehicles#oilspills#tracks
bg1GS-RGB3783,14176976-24
 GS-SWIR3852,288822--15
 GS-Therm3862,675778--25
 GS-UV3862,479897--15
bg3GS-RGB7286,7911,523--16
 GS-SWIR8006,6931,846--14
 GS-Therm7426,1831,485--19
 GS-UV7025,1171,657--16
 UAV-RGB2452,58059529--
bg4GS-RGB5894,1481,367162-42
 GS-SWIR7546,5912,09113-32
 GS-Therm6354,1451,471--26
 GS-UV7466,6692,20417-30
 UAV-RGB535453120--
bg5GS-RGB4861,792676--17
 GS-SWIR5831,7511,165--9
 GS-Therm499450861--12
 GS-UV530879995--6
bg7GS-RGB7562,7581,00969-24
 GS-Therm8101,1651,869--13
 UAV-Therm8088,8011,395408-44
 UAV-RGB6234,9432,3280--
bg9GS-RGB3571,06061114-14
 GS-SWIR3865811,094--7
 GS-Therm3911,037685--8
 GS-UV3876001,250--7
bg10GS-RGB4591,7981,051144-17
 GS-SWIR4491,2971,559--9
 GS-Therm4541,192972--6
 GS-UV3879861,340--11
 UAV-RGB3814,5031,20022--
bg11GS-RGB3915,2031,255--30
 GS-SWIR4094,0851,325--30
 GS-Therm4064,2171,102--33
 GS-UV4103,7711,356--30
 UAV-RGB5096,7001,235178--
bg12GS-RGB263776958--9
 GS-SWIR300753306--7
 GS-Therm29765236--5
 GS-UV301790416--7
bg13UAV-RGB3223,45392965--
cy1UAV-RGB9702,115399-939-
 UAV-Therm9691,795906--5
cy2UAV-RGB1,2041,569888-1201-
 UAV-Therm1,1933,9622,151--22
Total 24,219135,47951,1391,1972,140656
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Test data from the MAMMS-GL dataset for ground-based and low-altitude object detection and tracking.

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ScenarioSensor#images#persons#vessels#vehicles#oil spills#tracks
bg2GS-RGB8718,2332,506--26
 GS-SWIR8466,0692,197--14
 GS-Therm8455,6791,723--20
 GS-UV8455,1312,318--18
 UAV-Therm8426,7801,947--17
 UAV-RGB8024,0213,50700-
bg6GS-RGB6283,5361,485133-28
 GS-SWIR5693,5312,116--14
 GS-Therm5653,8511,823--15
 GS-UV5713,8182,038--16
 UAV-RGB1151,00461400-
bg8GS-RGB1,5267,4345,345--24
 GS-SWIR1,5155,0934,609--25
 GS-Therm1,5156,3374,046--22
 UAV-Therm1,5139,4557,426425-47
 UAV-RGB1,73912,7543,25900-
cy3UAV-RGB1,0724401,552-799-
 UAV-Therm1,0591,7141,840--9
Total 17,43894,88050,351558799295
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Training data from the MAMMS-GL dataset for the geolocation approximation task on a thermal UAV.

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Scenario#images#GNSS data points
cy1969906
cy21,1931,134
rd1514415
rd2588527
rd31,3881,388
rd4322322
rd5395337
rd6589529
Total5,9585,558
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Test data from the MAMMS-GL dataset for the geolocation approximation task on a thermal UAV.

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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Scenario#images#GNSS data points
bg7808697
cy4351343
cy5225225
cy6201201
rd7561464
Total2,1461,930
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MAMMS-OS (Optical Satellite):

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Dataset Structure: MAMMS-GL

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MAMMS-GL contains two folders:

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Below is the structure of each folder.

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detection-and-tracking

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detection-and-tracking/\n  train/\n    [scenario]/\n      [sensor type]/\n        annotations.xml\n        images/\n  test/\n    [scenario]/\n      [sensor type]/\n        annotations.xml\n        images/\n
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geolocation

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geolocation/\n  train/\n    [scenario]/\n      [sensor type]/\n        annotations.xml\n        annotations_geo.xml\n        images/\n        telemetry/\n  test/\n    [scenario]/\n      [sensor type]/\n        annotations.xml\n        annotations_geo.xml\n        images/\n        telemetry/\n
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Dataset Structure: MAMMS-OS

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The dataset contains two folders:

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