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