![\"Ortho](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/5916/media/6100-Frag_3.png\")
In this competition, Dstl provides you with 1km x 1km satellite images in both 3-band and 16-band formats. Your goal is to detect and classify the types of objects found in these regions.
\r\nThere are two types of imagery spectral content provided in this competition. The 3-band images are the traditional RGB natural color images. The 16-band images contain spectral information by capturing wider wavelength channels. This multi-band imagery is taken from the multispectral (400 – 1040nm) and short-wave infrared (SWIR) (1195-2365nm) range. All images are in GeoTiff format and might require GeoTiff viewers (such as QGIS) to view. Please refer to our tutorial on how to programmatically view the images.
\r\nAll imagery credit to: Satellite Imagery © DigitalGlobe, Inc.
\r\nIn a satellite image, you will find lots of different objects like roads, buildings, vehicles, farms, trees, water ways, etc. Dstl has labeled 10 different classes:
\r\nEvery object class is described in the form of Polygons and MultiPolygons, which are simply a list of polygons. We provide two different formats for these shapes: GeoJson and WKT. These are both open source formats for geo-spatial shapes.
\r\nYour submission will be in the WKT format.
\r\nIn this dataset that we provide, we create a set of geo-coordinates that are in the range of x = [0,1] and y = [-1,0]. These coordinates are transformed such that we obscure the location of where the satellite images are taken from. The images are from the same region on Earth.
\r\nTo utilize these images, we provide the grid coordinates of each image so you know how to scale them and align them with the images in pixels. You need the Xmax and Ymin for each image to do the scaling (provided in our grid_sizes.csv) Please refer to our tutorial on how to programmatically view the images.
\r\nfilename_to_classType = {
'001_MM_L2_LARGE_BUILDING':1,
'001_MM_L3_RESIDENTIAL_BUILDING':1,
'001_MM_L3_NON_RESIDENTIAL_BUILDING':1,
'001_MM_L5_MISC_SMALL_STRUCTURE':2,
'002_TR_L3_GOOD_ROADS':3,
'002_TR_L4_POOR_DIRT_CART_TRACK':4,
'002_TR_L6_FOOTPATH_TRAIL':4,
'006_VEG_L2_WOODLAND':5,
'006_VEG_L3_HEDGEROWS':5,
'006_VEG_L5_GROUP_TREES':5,
'006_VEG_L5_STANDALONE_TREES':5,
'007_AGR_L2_CONTOUR_PLOUGHING_CROPLAND':6,
'007_AGR_L6_ROW_CROP':6,
'008_WTR_L3_WATERWAY':7,
'008_WTR_L2_STANDING_WATER':8,
'003_VH_L4_LARGE_VEHICLE':9,
'003_VH_L5_SMALL_VEHICLE':10,
'003_VH_L6_MOTORBIKE':10}",
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"hasIsPublished": false,
"postTitle": "",
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{
"name": "Description",
"content": "The proliferation of satellite imagery has given us a radically improved understanding of our planet. It has enabled us to better achieve everything from mobilizing resources during disasters to monitoring effects of global warming. What is often taken for granted is that advancements such as these have relied on labeling features of significance like building footprints and roadways fully by hand or through imperfect semi-automated methods.![\"\"](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/5916/media/_competition_dstl.png\")
As these large, complex datasets continue to increase exponentially in number, the Defence Science and Technology Laboratory (Dstl) is seeking novel solutions to alleviate the burden on their image analysts. In this competition, Kagglers are challenged to accurately classify features in overhead imagery. Automating feature labeling will not only help Dstl make smart decisions more quickly around the defense and security of the UK, but also bring innovation to computer vision methodologies applied to satellite imagery.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "Submissions are evaluated on Average Jaccard Index between the predicted multipolygons and the actual multipolygons. This is a vector-based metric where we use polygon geometries to evaluate how well your predictions are aligned with the answer.
\r\nThe Jaccard Index for two regions A and B, also known as the \"intersection over union\", is defined as:
\r\n$$Jaccard = {\\frac{TP}{TP+FP+FN} }= {{|A \\cap B|}\\over{|A \\cup B|}} = {{|A \\cap B|}\\over{|A| + |B| - |A \\cap B|}}$$![\"Intersection](\"https://commons.wikimedia.org/wiki/File:Intersection_of_sets_A_and_B.svg#/media/File:Intersection_of_sets_A_and_B.svg\")
where TP is the true positives area, FP is the false positives area, and FN is the false negatives area.
\r\nFor each object class, of each image, we calculate the TP, FP, and FN areas. We then sum the total TP, total FP, and total FN across all the images, then the Jaccard is calculated for that class using total TP, total FP, and total FN. Then, we average all the Jaccard Indexes for all the 10 classes.
\r\nFor every row in the dataset, submission files should contain the following: ImageId (string), ClassType (int), and MultipolygonWKT (multipolygon coordinates in WKT format). You must submit a row for every ImageId and every ClassType. The complete format is in the sample_submission.csv file. If you want to predict an empty polygon for the class, put down MULTIPOLYGON EMPTY. Please submit all the rows in exactly the same order as sample_submission.csv, otherwise you might get a \"key not found error\".
\r\nThe file should contain a header and have the following format:
\r\nImageId,ClassType,MultipolygonWKT\r\n
6020_0_1, 1, MULTIPOLYGON EMPTY
6120_2_4,1,\"POLYGON ((0 0, 0.009188 0, 0.009188 -0.009039999999999999, 0 -0.009039999999999999, 0 0))\"
etc.
If you have complex polygons, it could take a longer time (4+ mins) to evaluate your submission. Please be patient when the submission is getting evaluated! If you find yourself getting timed out, please consider simplifying (smoothing) your polygons to reduce both the submission file size and the time it takes to evaluate the submission.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Prizes", "content": "You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
There is no maximum team size.
\r\nYou may submit a maximum of 3 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: 12/15/2016 12:00 AM UTC
\r\nMerger Deadline: 2/28/2017 11:59 PM UTC
\r\nEntry Deadline: 2/28/2017 11:59 PM UTC
\r\nEnd Date: 3/7/2017 11:59 PM UTC
\r\n\t\r\nCOMPETITION NAME (the 'Competition'): Dstl Satellite Imagery Feature Detection
\r\nCOMPETITION SPONSOR: Dstl
\r\nCOMPETITION WEBSITE: https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection
\r\nPRIZES: $100,000 total prize pool
\r\nThese are the complete, official rules for the Competition (the 'Competition Rules') and incorporate by reference the contents of the Competition Website listed above.
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\r\n'Submission' means the material submitted by you in the manner and format specified on the Website via the Submission form on the Website. You (or if you are part of a Team, your Team) may submit up to the maximum number of Submissions per day as specified above. All Submissions must be uploaded to the Website in the manner and format specified on the Website. Submissions must be received prior to the Competition deadline and adhere to the guidelines for Submissions specified on the Website.
\r\nSubmissions may not use or incorporate information from hand labeling or human prediction of the validation dataset or test data records.
\r\nIf the Competition is a multi-stage Competition with temporally separate training data and/or leaderboard data, one or more valid Submissions must be made and selected during each stage of the Competition in the manner described on the Competition Website.
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\r\nUnless otherwise permitted by the terms of the Competition Website, Participants must use the Data solely for the purpose and duration of the Competition, including but not limited to reading and learning from the Data, analyzing the Data, modifying the Data and generally preparing your Submission and any underlying models and participating in forum discussions on the Website. Participants agree to use suitable measures to prevent persons who have not formally agreed to these Competition Rules from gaining access to the Data and agree not to transmit, duplicate, publish, redistribute or otherwise provide or make available the Data to any party not participating in the Competition. Participants agree to notify Kaggle immediately upon learning of any possible unauthorized transmission or unauthorized access of the Data and agree to work with Kaggle to rectify any unauthorized transmission. Participants agree that participation in the Competition shall not be construed as having or being granted a license (expressly, by implication, estoppel, or otherwise) under, or any right of ownership in, any of the Data.
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\r\nA Submission will be ineligible to win a prize if it was developed using code containing or depending on software licensed under an open source license:
\r\n* other than an Open Source Initiative-approved license (see <http://opensource.org/>); or
* an open source license that prohibits commercial use.
This Competition is a challenge of skill and the final results are determined solely by leaderboard ranking on the private leaderboard (subject to compliance with these Competition Rules). Participants' scores and ranks on the Competition Website at any given stage of the Competition will be based on the evaluation metric described on the Competition Website, as determined by applying the predictions in the Submission to the ground truth of a validation dataset whose instances were a fixed set sampled from the Data.
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* require that you remediate within one week all issues identified in your Submission(s) (including, without limitation, the resolution of license conflicts, the fulfillment of all obligations required by software licenses, and the removal of any software that violates the software restrictions).
A tie between two or more valid and identically ranked submissions will be resolved in favour of the tied submission that was submitted first.
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\r\nAs a condition of receipt of the Prize, the Prize winner must deliver the final model’s software code as used to generate the winning Submission and associated documentation (consistent with the winning model documentation template available on the kaggle wiki at https://www.kaggle.com/wiki/WinningModelDocumentationTemplate) to the Competition Sponsor. The delivered software code must be capable of generating the winning Submission and contain a description of resources required to build and/or run the executable code successfully.
\r\nIf a potential winning Participant or Team member is a U.S. citizen, potential winner must also sign and return an IRS W-9 form, or if a foreign resident, an IRS W-8BEN form, within the stated time in order to claim the prize.
\r\nPotential winners will be disqualified and the prize may be awarded to an alternate winner (the next-ranked qualified Participant on the leaderboard) if
\r\n* the required documentation is not returned within 14 days after receipt of any request to provide documentation;
* prize notification letter/email or prize is returned as undeliverable; or
* potential winner or winning Team member is disqualified for any reason.
By accepting any Prize, each Participant receiving a Prize thereby agrees to use of his/her name, address, likeness and/or Prize information by Kaggle and Competition Sponsor for promotional purposes in any medium without additional compensation.
\r\nAs a further condition of receipt of a Prize, each winning Participant thereby licenses their winning Submission and the source code used to generate the Submission according to the Winner License Type specified above (note: if no Winner License Type is specified above, the Winner License Type is deemed to be Non-Exclusive License).
\r\nIf the Winner License Type for the Competition (see Winner License Type above) is a Non-Exclusive License then each Winner by accepting a Prize thereby:
\r\n* grants to Competition Sponsor and its designees a worldwide, non-exclusive, sub-licensable, transferable, fully paid-up, royalty-free, perpetual, irrevocable right to use, not use, reproduce, distribute, create derivative works of, publicly perform, publicly display, digitally perform, make, have made, sell, offer for sale and import their winning Submission and the source code used to generate the Submission, in any media now known or hereafter developed, for any purpose whatsoever, commercial or otherwise, without further approval by or payment to Participant; and
* represents that he/she/it has the unrestricted right to grant that license.
If the Winner License Type for the Competition (see Winner License Type above) is Open Source License, then each Winner by accepting a Prize thereby:
\r\n* licenses their winning Submission and the source code used to generate the Submission under the MIT License (an open source software license commonly described at <http://opensource.org/licenses/MIT>), unless another open source license is chosen explicitly from the list of those approved by the Open Source Initiative at <http://opensource.org/licenses/>; and
* represents that he/she/it has the unrestricted right to grant that license.
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\r\nTeam membership may not exceed the Maximum Team Size.
\r\nTeams may be permitted to merge at Kaggle’s discretion, so long as the merged Team meets all requirements in these Competition Rules. Kaggle will review any merger request and will either approve or reject the request within three business days. Merger requests may be rejected if the combined number of Entries made by the merging Teams exceeds the number of Entries permissible at the date of the merger request. Team merger requests will not be permitted within seven days of any deadline listed on the Competition Website.
\r\nIf a Team wins a monetary Prize, Competition Sponsor will allocate the Prize money in even shares between the Team members unless the Team unanimously contacts Kaggle via the Competition Website within three business days following the Submission deadline to request an alternative prize distribution.
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* does not contain confidential information or trade secrets and is not the subject of a registered patent or pending patent application;
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* was not previously published and has not won any other prize/award.
A breach of any of these warranties will result in the corresponding Submission being invalid.
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\r\nYou agree that these terms and the relationship between you and Competition Sponsor shall be governed by the laws of the State of California and the United States of America.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "All deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Data Processing Tutorial", "content": "We have found these packages helpful:
\r\nPython - shapely.loads()
R - rgeos
\r\nWe have found these packages helpful:
\r\nPython - json, geojson, shapely (use json, then convert into shapely's MultiPolygon)
R - geojsonio
\r\nIn this dataset that we provide, we create a set of geo-coordinates that are in the range of x = [0,1] and y = [-1,0]. These coordinates are transformed such that we obscure the location of where the satellite images are taken from. The images are from the same region on Earth.
\r\nTo utilize these images, we provide the grid coordinates of each image so you know how to scale them and align them with the images in pixels. In grid_sizes.csv, you are given the Xmax and Ymin values for each imageId.
\r\nFor each image, you should be able to get the width (W) and height (H) from the image raster. For a 3-band image that is 3391 x 3349 x 3, W is 3349, and H is 3391. Then you can scale your data as follows:
\r\n$$W^\\prime = W \\cdot \\frac{W} {W+1}$$
\r\n$$x^\\prime = \\frac{x}{x_{max}} \\cdot W^\\prime$$
$$H^\\prime = H \\cdot \\frac{H}{H+1}$$
$$y^\\prime = \\frac{y}{y_{min}}\\cdot H^\\prime$$
\r\nIt's best to use cascaded_union() to flatten a list of polygons into a non-overlapping multipolygon.
\r\nScaling your results back to the original coordinates:
\r\n$$ x = x^\\prime \\cdot \\frac{x_{max}}{W^\\prime}$$
\r\n$$ y = y^\\prime \\cdot \\frac{y_{min}}{H^\\prime}$$
\r\nThen it's easy to use shapely to output the multipolygon to wkt format: multipoly.wkt
GDAL is great but a bit difficult to install. If you just want to read the files as rasters, tifffile is a lightweight package to open the GeoTiff files.
\r\nFor example, the following code reads you the size of the 16-band images:
\r\nimport gdal\r\n
from gdalconst import *
img_filename_16bandA = 'data/16band/6100_1_3_A.tif'
img_filename_16bandM = 'data/16band/6100_1_3_M.tif'
img_filename_16bandP = 'data/16band/6100_1_3_P.tif'
datasetA = gdal.Open(img_filename_16bandA, GA_ReadOnly )
datasetM = gdal.Open(img_filename_16bandM, GA_ReadOnly )
datasetP = gdal.Open(img_filename_16bandP, GA_ReadOnly )
print 'Size is ',datasetA.RasterXSize,'x',datasetA.RasterYSize, \\
'x',datasetA.RasterCount
print 'Size is ',datasetM.RasterXSize,'x',datasetM.RasterYSize, \\
'x',datasetM.RasterCount
print 'Size is ',datasetP.RasterXSize,'x',datasetP.RasterYSize, \\
'x',datasetP.RasterCount
Outputs:
\r\nSize is 136 x 134 x 8\r\n
Size is 848 x 837 x 8
Size is 3391 x 3348 x 1
Or you could use tifffile:
import tifffile as tiff\r\n
P = tiff.imread(img_filename_16bandP)
tiff.imshow(P)
Thanks to community member smota's post:
\r\ndevtools::install_github(\"ropensci/geojsonio\")\r\n
library(\"geojsonio\")
install.packages(\"rgdal\", type = \"source\")
install.packages(\"rgeos\", type = \"source\")
library(\"rgdal\")
library(\"rgeos\")
library(ggplot2)
grid_6010_4_4 <-geojson_read(\"./data/train_geojson/train_geojson/6010_4_4/Grid_6010.geojson\", method = local, what= 'sp')
plot(grid_6010_4_4)
ggplot(grid_6010_4_4, aes(long, lat, group = group)) + geom_polygon()
To access images:
\r\nlibrary(raster)\r\n
raster_6044_4_4 <- raster(\"./data/three_band/6040_4_4.tif\")
plot(raster_6044_4_4)
library(rgdal)
gdal_6044_4_4 <- readGDAL(paste0(\"./data/three_band/\", '6040_4_4', \".tif\"))
plot(gdal_6044_4_4)
Yes! This dockerfile should be useful for Python users.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }