forestry v01
TRANSCRIPT
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Intelescope
IntelescopeEmpowering Agro Business
Through High-Resolution Aerial Remote Sensing
for Precision Agriculture
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Core Technology & Target Opportunity
1 .
Recognizing & classifying high resolution remotely acquired image data is the core technology of
Intelescope. Precision agriculture, mining and urban planning are potential applications
Intelescope blends expertise
in Remote Sensing, Image
Recognition & Agronomy to
improve agricultural yield.
Remote sensing and imagerecognition technology have
wide applicability above and
beyond agriculture.
Mining applications use
remotely acquiredhyperspectral images.
RemoteSensingImage
Recognition
PrecisionAgriculture
Military Target
Acquisition
Robotics /Computer
Vision
Urban-Planning
Infrastructure
Traffic Detection
Mining &
NaturalResources
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Rich Data in Geographical Information Systems
1
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Next Phase R&D
1
Briefcase deployable Mapping
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Agenda
Land Parcel Due Diligence, Production Auditing & Land Use
Forestry
Field Crops
UrbanApplications, Counting Cows & Misc
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Demonstration of Due DiligenceAbilities
Following sequence ofimages demonstrate operational resolution & object recognition capabilities.
Isolation of
parcel
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Cropped Parcel Determine Area ofInterest
Potential land parcel acquisitions
can be scrutiniz
ed through aerial
analysis.
Due diligence process is simplified
for very large parcels in difficult to
access areas
Analysis deliverables:
Precise (25cm) geo-
referenced orthophoto map
Precise gradient information
for each pixel (GSD 15cm X
15cm)
Detection of rocks and debris
inhibiting soil conditions
Acidity & fertilization map
(correlated with soil samples)
Soil thickness & water
retention ability
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Area of interest @ 480cm / pixel
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@ 240cm / pixel
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@ 120cm / pixel
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@ 60cm / pixel
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@ 30cm / pixel
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High resolution multispectral aerialimages facilitate the due diligence process.
High Resolution Object Detection for Land Parcel Due Diligence
15cm/Pixel
3.22 m
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Land Use: Classification & Auditing
Patria Soragna (near Orthophoto, GSD 25cm)
Rectified Geo-Referenced Mosaic of
Aerial Photographs in red, green & NIR
bands.
Interpreted Geo-
Referenced Image
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Each plot type is classified through geometric & radiometric matching .The base image from which
this collage is composed was taken in NIR, Red and Green bands.
Land Classification w/ Legend
Farmed areas
Full vegetation cover
Farmed areas
partial vegetation cover
Water
Constructions
Woodland / Trees
Pasture
Vines
Tree cultivation
Unused
Border delineation
Legend
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Land Use Audit: Automatic Detection of Vineyards
This image is part of a project for Italian Agriculture Ministry on whether subsidies were
appropriately used to seed & expand vineyards
Automatic detection of
rows and breaks in
vineyards. (Image is in
RGB + CIR)
Image interpreted andgeo-referenced. Relevant
aspects (such as length
of rows, breaks in rows,
seeded area) are stored
in geographic
information database.
Thuschanges overtime can be
tracked automatically.
Analysis Details
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Agenda
Land Parcel Due Diligence, Production Auditing & Land Use
Forestry
Field Crops
UrbanApplications, Counting Cows & Misc
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Each tree in field is counted (labeled with precise GPS location). The biomass of plantation is
measured. This can be used to obtain TER Carbon Credits.
Automatic Tree Recognition & Counting for Carbon Credits
Tree story
+
Height AnomalyID: 47.521990 W
23.593409 S
Euc3301-21/06/06
Slope: 5%
Soil: Terra Rosa
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Tree story
Forestry: Extracting Data from Aerial Photographs
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In this example gaps in tree planting are automatically classified, and measured
Tree Counting: Object Recognition & Classification
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Location of the Pilot
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Area: 16,000 hectares
Photography Date: 24/07/2008
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Areas ofinterests
Sample of 5
plantations
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Plot 15D010_a
15D010_a
15D010_b
15D011_a
15D011_b15D011_c
Plots names
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PlantingHoles Recognition
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Tree Counting
RMS Error: 5%
Each Tree Gets an XYEach Tree Gets an XYCoordinateCoordinate
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SievingHoles according to Size
5-10 m2
10-15 m2
10-20 m2
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Sieving Holes according to
Length-Width Ratio Index1 - 1.5 LWRI
1.5 - 2 LWRI
2 - 3 LWRI
3 - 4 LWRI
< 4 LWRI
Holes
Not Holes
(Row Gaps)
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Orthophoto
Height cross section
Orthophoto 3D view
Taller trees
0m
1m
2m
3m
4m
10m 20m 30m 40m 50m
0m
1m
2m
3m
4m
10m 20m 30m 40m 50m
oad Smaller trees
eight
Distance
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Tree height variability
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Tree height variability
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Orthophoto
Sample
plot
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Plot zoom in
Individual tree mapping
Total: 23,568 trees
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Individual tree mapping
GeoGeo--location is assigned tolocation is assigned to
each treeeach tree
Stand extraction
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Surface elevation model
High
Low
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Terrain elevation model
High
Low
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Plot diagonal view
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Surface elevation model
High
Low
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Terrain elevation model
High
Low
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Tree height extraction
Surface
Terrain
Tree Height
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Tree height model
5 m
3 m
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Individual tree height
5 m
3 m
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Tree height layer + Tree count
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Individual tree height 3D
5 m
3 m
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Plot inventory report
TreeID Xcoordinate
Ycoordinate
Tree height(m)
Actual stand(trees/ha)
1 7988368 427345 3.50 1050
2 7988369 427345 3.25 1050
3 7988369 427348 4.00 1000
4 7988372 427348 4.50 950
5 7988372 427351 4.25 950
6 7988374 427351 4.50 950
7 7988374 427354 4.75 800
8 7988376 427354 5.00 800
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Vegetative intensity in Soybean and Cotton (using the spectral
index). Exposure of fertilization defects, Brazil.
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CropHeight (2m)
BiomassBiomass
VolumeVolume
RTS / ATR (total recovered sugars) can be accurately measured. This information can be used to
hedge production. Sugar Cane financers can audit their clients with this information.
Measuring the Biomass and Sugar Cane ATR (RTS)
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Weed Detection Through Spectral Analysis
Specific Localization of Weeds facilitates less use of herbicides saving costs and minimizing
pollution. Eliminating weeds improves agricultural yields.
OrthoPhoto RGB+CIR Weed localization with GPS coordinates
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Object Detection: Counting Cows
Thermal imaging (middle image) can accurately pinpoint cows on a field. Red Xs (right most image)
denote the location of each detected cow