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Dataset for Surface Landmine detection. Videos are taken in Italy (Faculty of Engineering, Florence) and USA (Franklyn and Marshal college, Philadelphia).

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SULAND: SUrface LANDmine (detection)

This is the official dataset for the paper Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging. repo is about Surface Lidar Image Analysis. Our aim is to find surface land-mine such as butterfly and star-fish.

Get started

You need to create a python venv following command:

$ python3 -m venv venv
$ source venv/bin/activate

Dataset creation

The dataset has been collected with iPhone13-Lidar and record-3d software and application. For information about the software, refer to the record-3d github repository. For information about dataset environmental settings and procedure, feel free to open an issue.

Dataset Annotations

The data collected are annotated using CVAT software. Annotation are done both on their server, and locally. To use their server, please refer to the webpage. To set up your own CVAT server, please refer to the documentation.

Data in YOLO format

You can get the ITA and USA datasets from MICC dataset-SULAND

These are the IID and OOD data, respectivelly. We advise you to download them (or link them with a sym-link) into the two folders:

/SURLAND-Dataset
    /data-iid   <-- ITALY dataset
        /ITA.yaml
        /yolo.json
        /train
        /val
        /test
    /data-ood   <-- USA dataset
        /USA.yaml
        /yolo.json
        /val
        /test
    /docs
    /examples
    /venv
    LICENSE
    README.md
    requirements

Dataset split

For the Italian data, we decided to split the data based on the video numbers as follow:

test = [2, 10, 19, 27, 36, 44]
val = [3, 7, 15, 26, 33]
train = [1, 4, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32, 34, 35, 37, 39, 40, 41, 42, 43, 45, 46, 47]

This because, as you can appreciate from the excell file that describe all videos, we wanted to have similar distribution of data across all the three splits.

For the USA data, however, we decided to provide all videos as test split as those Out of Distribution data are only used at inference:

test = [1,2,3,4,5,6,7,8,9,10]

In the paper you can find the statistics about the datasets and the splits. Raw information are shown in the following table:

Split Video Title Durata (s) Ann. (%) n° frames ann. n° frames Environment Weather Orientation Slope
train ITA 1 28,83 31,79% 55 173 Grass Cloudy Vertical Low
test ITA-2 80,17 47,61% 229 481 Gravel Cloudy Vertical Low
val ITA 3 23,17 36,69% 51 139 Grass Cloudy Vertical Low
train ITA-4 75,00 39,11% 176 450 Gravel Cloudy Vertical Low
train ITA-6 24,50 20,41% 30 147 Grass Sunny Vertical Low
val ITA-7 75,17 19,96% 90 451 Grass Sunny Vertical Low
train ITA-8 105,33 10,60% 67 632 Grass Sunny Vertical Low
train ITA-9 41,83 17,53% 44 251 Grass Sunny Vertical Low
test ITA-10 30,17 24,31% 44 181 Grass Shadow Vertical Low
train ITA-11 20,00 40,00% 48 120 Grass Sunny Vertical Low
train ITA-12 121,33 24,86% 181 728 Gravel Shadow Vertical Low
train ITA-13 125,83 32,05% 242 755 Gravel Shadow Vertical Low
train ITA-14 24,50 100,00% 147 147 Grass Sunny Vertical Low
val ITA-15 96,00 27,43% 158 576 Gravel Shadow Vertical Low
train ITA-16 98,83 28,16% 167 593 Gravel Shadow Vertical Low
train ITA-17 118,33 17,32% 123 710 Grass Sunny Vertical Low
train ITA-18 129,50 10,42% 81 777 Grass Sunny Vertical Low
test ITA-19 111,33 10,48% 70 668 Grass Sunny Vertical Low
train ITA-20 124,50 10,17% 76 747 Grass Sunny Vertical Low
train ITA-21 128,33 26,62% 205 770 Grass Shadow Vertical Low
train ITA-22 112,17 8,02% 54 673 Grass Shadow Vertical Low
train ITA-23 126,50 6,59% 50 759 Grass Sunny Vertical Low
train ITA-24 126,17 9,64% 73 757 Grass Shadow Vertical Low
train ITA-25 137,17 10,57% 87 823 Grass Sunny Vertical Low
val ITA-26 120,17 9,71% 70 721 Gravel Shadow Vertical Low
test ITA-27 121,83 6,57% 48 731 Gravel Shadow Vertical Low
train ITA-28 128,00 9,24% 71 768 Gravel Shadow Vertical Low
train ITA-29 130,67 14,54% 114 784 Gravel Shadow Vertical Low
train ITA-30 144,33 7,97% 69 866 Gravel Cloudy Vertical Low
train ITA-31 120,17 15,12% 109 721 Grass Cloudy Vertical Low
train ITA-32 120,67 39,23% 284 724 Grass Shadow Vertical Medium
val ITA-33 120,67 27,35% 198 724 Grass Shadow Vertical Medium
train ITA-34 117,67 39,52% 279 706 Grass Sunny Vertical High
train ITA-35 121,00 17,22% 125 726 Grass Sunny Vertical High
test ITA-36 122,00 28,55% 209 732 Grass Sunny Vertical High
train ITA-37 129,17 36,00% 279 775 Gravel Sunny Vertical High
train ITA-39 133,00 30,08% 240 798 Grass Cloudy Vertical High
train ITA-40 125,17 34,49% 259 751 Grass Shadow Vertical High
train ITA-41 127,83 18,38% 141 767 Grass Shadow Vertical High
train ITA-42 137,17 34,63% 285 823 Grass Sunny Vertical High
train ITA-43 150,00 32,56% 293 900 Grass Sunny Vertical High
test ITA-44 158,33 25,16% 239 950 Grass Shadow Vertical High
train ITA-45 140,67 28,91% 244 844 Grass Shadow Vertical High
train ITA-46 150,00 25,33% 228 900 Grass Cloudy Vertical High
train ITA-47 148,33 34,61% 308 890 Grass Cloudy Vertical High
val USA-1 39,00 71,79% 168 234 Grass Sunny Vertical High
val USA-2 56,17 90,50% 305 337 Grass Sunny Vertical Medium
val USA-3 63,50 87,66% 334 381 Grass Sunny Vertical High
val USA-4 87,83 77,23% 407 527 Grass Shadow Vertical Medium
val USA-5 80,83 70,10% 340 485 Grass Cloudy Vertical Low
val USA-6 80,17 76,51% 368 481 Grass Sunny Vertical Low
val USA-7 74,33 69,06% 308 446 Grass Sunny Vertical Low
val USA-8 81,00 77,57% 377 486 Grass Cloudy Vertical Low
val USA-9 80,83 84,54% 410 485 Grass Sunny Vertical Low
val USA-10 95,00 89,82% 512 570 Grass Sunny Vertical Low

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Dataset for Surface Landmine detection. Videos are taken in Italy (Faculty of Engineering, Florence) and USA (Franklyn and Marshal college, Philadelphia).

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