computer vision for satellite data applications · ground data availability ground data alignment...

DMU-CMS-SUPSC03-PRE-11-E © DEIMOS Space UK Ltd.

Elecnor Deimos is a trademark which encompasses Elecnor Group companies that deal with Technology and Information Systems: Deimos Space S.L.U., Deimos Castilla La Mancha S.L.U., Deimos Engenharia S.A., Deimos Space UK Ltd., Deimos Space S.R.L. (Romania).

Computer Vision for Satellite Data Applications

Paula Martí

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DMU-CMS-SUPSC03-PRE-11-E © DEIMOS Space UK Ltd. 2

• Elecnor Deimos is the technology company of the Elecnor Group, operating in Aerospace, Defence, Transport, Energy, Environment, etc.

• Located in 4 countries, 300+ engineers:

o Spain (HQ), Portugal, UK, Romania

• Space is our core business. Experts in satellite software and systems engineering:

o Flight Systems

o Ground Systems & Space Situational Awareness

o Satellite Navigation

o Earth Observation & Satellite Data Applications

o Space Missions (Deimos-1,2)

Deimos Space UK


About me

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Paula Martí

• Technical Manager at Deimos UK

o Remote sensing applications

o Satellite data processing

o Management and business development

• Education:

o MSc Telecommunications EngineeringUniversidad Politécnica de Valencia2007

o MSc Computer VisionOxford Brookes University, UK2015



Why is computer vision important for the geoinformation world?

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What we do with satellite data

Feature extraction and counting:

• Trees to help calculate insurance value

• Polar bears to ensure sustainable population levels

• Number of plants in a field to estimate yield

• Counting Graves to map cemeteries

• 3D modelling

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Source: http://gisgeography.com/100-earth-remote-sensing-applications-uses/

Classifying the land:

• Extent of the different types of land and its use

• Mapping mineral deposits with hyperspectral data

Finding boundaries:

• Riparian areas: border between land and a river or a lake

• Forest fires

• Urban growth



Satellite data applications using classic methods of Machine Learning

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CropID: Crop Classification for the whole of UK

Satellite data plus geometry of the field

boundaries

Apply machine learning algorithms

(Random forest, SVM)

Classify by choosingthe most probable crop for each field


SAFIY: Urban Mapping

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Objective:

Automatically update maps in Dubai using high-resolution satellite imagery


SAFIY: Road detection with SVM

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Machine learning

algorithms to

classify road

pixels

Vectorise and

update mapChallenges

- Shadows are often

misclassified

- It might not work on

all types of roads

- Simple, straight

vectors are difficult

to extract


SAFIY: Feature extraction

Roundabout detection

1 – Create a feature descriptor that is able to ‘describe’ a roundabout

2 – Find circles in the image

3 – Classify the circles to discard round features that are not roundabouts

4 - Vectorise

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Modern computer vision techniques applied to satellite data

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Urban 3D Reconstruction

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Techniques:

• General-purpose 3D reconstruction from images: stereo, wide-baseline matching, camera calibration, and point triangulation

• 3D reconstruction using LIDAR

• Extrusion from footprint

• Computer Vision techniques such as Gaussian Process Latent Variable Models (GPLVM)


Urban 3D Reconstruction

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GPLVM:

• Trained with 3D

models of the

buildings.

• Create a latent

variable space of what

a building looks like.

• Given the outline, the

best shape for the

building can be fitted.

• Only 1 view needed.

Input Input

Output Output


Road detection using Deep Learning

Apply Deep Learning algorithms to previous data

Training data:

We only used 5 Deimos-2 images cut into 48x48 pixel tiles (~100 tiles)

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Road detection using Deep Learning – some results

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Challenges

Ground data availability

Ground data alignment with satellite data

Deep learning is demanding in terms of processing power and memory

• 8-layer Fully Convolutional Network VGG provided by Oxford University

• Rented AWS most powerful machine with GPU and TensorFlow

• Train the network with small tiles

• Result: Machine runs out of memory!

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Current projects

Palm tree detection Car counting


Collaborations

We are happy to discuss:

- Detection of features of interest to other users

- Ground data

- Ways to easily label data

- Processing capabilities

- Experts in TensorFlow

- Different algorithms that you might want to try with our data and labels to compare results

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Thank you

[email protected]

computer vision for satellite data applications · ground data availability ground data alignment...

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