SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING Tornado Damage. L3HARRIS SOLVE GEOSPATIAL PROBLEMS WITH

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  • SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    And an introduction to the recently updated ENVI Deep Learning Module

    May 12, 2020 Zach Norman and Bill Okubo – Product Management

  • L3HARRIS 2SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Contact Information and Introductions

    Zachary Norman

    Product Manager

    zachary.norman@l3harris.com

    Bill Okubo

    Product Manager

    bill.okubo@l3harris.com

  • L3HARRIS 3SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Agenda

    Introduction

    Scenario One: Environmental Monitoring

    Scenario Two: Disaster Response

    • Hands-on Tutorial Overview

    • Change Detection with Deep Learning

    The ENVI Deep Learning Module

    Additional Examples

    Questions and Discussion

  • L3HARRIS 4SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    EMPLOYEES

    50K~ LOCATIONS IN

    COUNTRIES

    30~ LOCATIONS

    400~ CUSTOMERS IN

    COUNTRIES

    130~

    L3Harris Technologies is an agile,

    global aerospace and defense

    technology innovator, delivering

    end-to-end solutions that meet

    customers’ mission-critical needs.

  • L3HARRIS 5SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Automatic Language Translation

    Driverless Cars Caption Generation

    Social Media & Online Shopping

    What is Deep Learning: Applications as a Glance

    Object Detection

  • L3HARRIS 6SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Environmental Applications of Deep Learning

    Deep learning is poised to work well for large-scale

    monitoring applications, such as the impact of the oil

    and gas industry

    There can be regulations for companies to return the

    environment to its original state

    Information derived from deep learning can be used to

    keep industry accountable or can help companies verify

    they are on track with government regulations

    1 2

    3 1. A well pad preceding reclamation. BLM photo.

    2. Native plants in the first stage of

    vegetative growth since reclamation.

    BLM photo.

    3. Yucca, Shinnery Oak, and various

    native shrub and grass species serve as

    evidence of successful reclamation

    efforts. BLM photo.

    Source: https://www.blm.gov/blog/2018-05-15/reclamation-success-stories-new-mexico-state-office

    Success story from the U.S. Bureau of Land Management (BLM) showing reclamation progress

    over time

    https://www.blm.gov/blog/2018-05-15/reclamation-success-stories-new-mexico-state-office

  • L3HARRIS 7SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Well Pads in Texas

    31.527075°N, 102.062456°W N

    This shows just how

    prevalent the oil and gas

    industry is in some parts

    of the US and why it is

    important to monitor the

    environment

  • L3HARRIS 8SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Well Pad Extraction with ENVI Deep Learning

    How did we solve this problem?

    • Used PlanetScope 4 band data

    • 15 scenes from the Texas and Oklahoma area of the U.S.

    • 11,202 labels as point features for rapid labeling

    • Custom training parameters:

    – Loss weight = 0.0

    – Class weight = [2,3]

    Training performance: 99.5% accuracy, 89.5% precision, 80% recall after 2.5 hours of training

    IDL workflow using the

    ENVI API that

    completely automates

    the data preparation

    and training process

    Yes, it is only 12 lines

    of code!

  • L3HARRIS 9SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Well Pads in Texas

    31.527075°N, 102.062456°W N

    This shows just how

    prevalent the oil and gas

    industry is in some parts

    of the US and why it is

    important to monitor the

    environment

  • L3HARRIS 10SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Detected Well Pads

    31.527075°N, 102.062456°W N

    ENVI Deep Learning

    detected nearly 2900

    well pads in this area

  • L3HARRIS 11SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Lessons Learned and Takeaways

    Easier Detects Harder Detects

  • L3HARRIS 12SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    What Comes Next: Assessing Environmental Recovery

    Once you know where well pads were, monitoring them

    is easy with the out-of-the-box tools in ENVI

    Change detection, spectral indices, or other spectral

    analysis in ENVI to monitor vegetation returning to its

    original state

    Looking at something as simple as the NDVI (Normalized Difference Vegetation Index) over well pads which, over

    time, could be used to verify vegetation has returned after the site is cleared

    1. Hot-spot analysis

    2. Vegetation health

    classification

    1 2

  • L3HARRIS 13SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Disaster Response

    As we understand, some of the top priorities after a

    disaster are to:

    1. Rapidly assess the extent of the damage

    2. Task resources to help in the recovery effort based on

    where, and how severe, the damage is

    With deep learning, once you have the data, you can

    reduce the timeline down to hours rather than days

    For reference, it can take up to 6 days to get accurate

    maps on where damage has occurred after disasters like

    tornadoes

    Examples of different natural disasters and how you can see them with remotely sensed data. Examples: Fire extent (top-left and lower-left), landslides (top-right),

    flooding (lower-right).

  • L3HARRIS 14SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Tornado Damage

  • L3HARRIS 15SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Hands-on Tutorial: Tornado Damage Detection

    Label

    “Here are some examples of the feature I’m interested in”

    Train “Learn what the feature looks like”

    Classify “Find more features like this elsewhere”

    Use the Deep Learning Labeling Tool to create your classes, manage your training data, and automate

    the training process

    Use the ENVI ROI tool

    to create accurate

    training data using

    points, lines, or

    polygons

  • L3HARRIS 16SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Hands-on Tutorial: Tornado Damage Detection

    Label

    “Here are some examples of the feature I’m interested in”

    Train “Learn what the feature looks like”

    Classify “Find more features like this elsewhere”

    Get real-time feedback on the performance of your models using TensorBoard which is included in the

    ENVI Deep Learning Module

  • L3HARRIS 17SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Hands-on Tutorial: Tornado Damage Detection

    Label

    “Here are some examples of the feature I’m interested in”

    Train “Learn what the feature looks like”

    Classify “Find more features like this elsewhere”

    Seamless integration with ENVI to easily classify your imagery, visualize results, cleanup results, and

    generate vectors of your detections

  • L3HARRIS 18SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Other Applications of Damage Detection with Deep Learning

    We can also use change detection with deep learning,

    including two separate images over the same area and

    just focus on extracting the changes in the scene

    Source data from the “xView 2: Assess Building

    Damage” challenge (https://xview2.org/)

    Data:

    • Sensor: Maxar Worldview

    • Images cover the same spatial extent and are

    registered

    • Used IDL to parse training data and create the labels

    for training

    Time 1 Time 2

    https://xview2.org/

  • L3HARRIS 19SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    A Look at the Details

    Use the ENVI Modeler to automate your deep learning workflow, train with the click of a button,

    and not write a single line of code!

  • L3HARRIS 20SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Results

  • L3HARRIS 21SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    The ENVI Deep Learning Module – Making Deep Learning Easy

    Applied deep learning for geospatial imagery in ENVI, the

    leading remote sensing and image analysis software

    Without needing to program, the capabilities include:

    • Segmentation (i.e. cloud masking, road surface)

    • Object detection (i.e. cars, ships)

    • Linear feature extraction (i.e. roads, rivers)

    • Support for nearly any image format and data modality

    • Can use points, lines, and polygons for trainingCreate Training

    Data

    Create Models

    Evaluate Performance

    Deep learning workflow in ENVI, built on TensorFlow and

    Keras

    Assess building damage after hurricanes and

    tornadoes

    Automated flood detection using SAR

  • L3HARRIS 22SOLVE GEOSPATIAL PROBLEMS WITH DEEP LEARNING

    Deep Learning Guide Map

    Entry point t