This document summarizes a project to generate an enhanced global digital elevation dataset by merging existing topographic data sources. The project aims to fill voids and improve accuracy of the Shuttle Radar Topography Mission (SRTM) dataset using supplementary data like the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM). The merged dataset will be evaluated using laser altimetry from the Ice, Cloud, and land Elevation Satellite (ICESat) and distributed to support research.
• “Detecting radio-astronomical "Fast Radio Transient Events" via an OODT-based metadata processing pipeline”, Chris Mattmann, Andrew Hart , Luca Cinquini, David Thompson, Kiri Wagstaff, Shakeh Khudikyan. ApacheCon NA 2013, Februrary 2013
• “Detecting radio-astronomical "Fast Radio Transient Events" via an OODT-based metadata processing pipeline”, Chris Mattmann, Andrew Hart , Luca Cinquini, David Thompson, Kiri Wagstaff, Shakeh Khudikyan. ApacheCon NA 2013, Februrary 2013
From pixels to point clouds - Using drones,game engines and virtual reality t...ARDC
Presentation by Dr Tim Brown
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Individual snippet:https://youtu.be/PVf4zYNJlmM?list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435
Sensors and Crowd - Steve Liang, GeoCENS ProjectCybera Inc.
Steve Liang, assistant professor at the University of Calgary, presented these slides as part of the Cybera Summit 2010 session, Environmental Infrastructure: The Tools and Technologies Behind Water and Resource Management.
Using Apache ACE as a distribution and management platform for a large--and growing-- number of embedded devices in the field.
I used this presentation at Apachecon NA 2010.
I'm more about story and images than about text on slides, you can try to follow along here.
We Rewind motor by re-designing its winding data that will meet its supply voltage using EASA AC Motor Redesign Software and Computerize Motor Rewinding machine.
Media Mixer semantic technologies for UGC copyright management por Roberto Ga...ACTUONDA
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Primer encuentro BIG MEDIA
Conectando Media, Audiencia y Publicidad con Datos
24 de junio 2014, Madrid
• Sponsor Platinum : Perfect Memory
• Sponsor Gold : Stratio, Paradigma
• Con el apoyo de : Big Data Spain, Medios On
• Socio tecnológico : Agora News
• Organizadores : Actuonda y Cátedra Big Data UAM-IBM
• Contacto : Nicolas Moulard (Actuonda) moulard@actuonda.com @Radio_20
On 17/10/2013 TU Delft Climate Institute organised the symposium The Greenland and Antarctic ice sheets: present, future, and unknowns. This is one of the four presentations given there.
http://www.tudelft.nl/nl/actueel/agenda/event/detail/symposium-tu-delft-climate-institute-17th-october-2013/
From pixels to point clouds - Using drones,game engines and virtual reality t...ARDC
Presentation by Dr Tim Brown
Full webinar: https://www.youtube.com/watch?v=bl_7ClXhQlA&list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435&index=11
Individual snippet:https://youtu.be/PVf4zYNJlmM?list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435
Sensors and Crowd - Steve Liang, GeoCENS ProjectCybera Inc.
Steve Liang, assistant professor at the University of Calgary, presented these slides as part of the Cybera Summit 2010 session, Environmental Infrastructure: The Tools and Technologies Behind Water and Resource Management.
Using Apache ACE as a distribution and management platform for a large--and growing-- number of embedded devices in the field.
I used this presentation at Apachecon NA 2010.
I'm more about story and images than about text on slides, you can try to follow along here.
We Rewind motor by re-designing its winding data that will meet its supply voltage using EASA AC Motor Redesign Software and Computerize Motor Rewinding machine.
Media Mixer semantic technologies for UGC copyright management por Roberto Ga...ACTUONDA
Media Mixer semantic technologies for UGC copyright management por Roberto Garcia de Universidad de Lleida
Primer encuentro BIG MEDIA
Conectando Media, Audiencia y Publicidad con Datos
24 de junio 2014, Madrid
• Sponsor Platinum : Perfect Memory
• Sponsor Gold : Stratio, Paradigma
• Con el apoyo de : Big Data Spain, Medios On
• Socio tecnológico : Agora News
• Organizadores : Actuonda y Cátedra Big Data UAM-IBM
• Contacto : Nicolas Moulard (Actuonda) moulard@actuonda.com @Radio_20
On 17/10/2013 TU Delft Climate Institute organised the symposium The Greenland and Antarctic ice sheets: present, future, and unknowns. This is one of the four presentations given there.
http://www.tudelft.nl/nl/actueel/agenda/event/detail/symposium-tu-delft-climate-institute-17th-october-2013/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
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Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Neuro-symbolic is not enough, we need neuro-*semantic*
A Merged Global Digital Topographic Data Set - Progress Report.pdf
1. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
IGARSS 2011
Dr. Michael Kobrick
Dr. Robert Crippen
Dr. Thomas Farr
Jet Propulsion Laboratory
July 27, 2011
2. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Objective:
Generate best quality global digital elevation data set
at highest possible resolution by combining best
existing data
Supported by NASA *MEaSUREs program (M. Maiden)
for enhancement of SRTM data set
* Making Earth Science Data Records
for Use in Research Environments
3. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
SRTM: Shuttle Radar Topography Mission
Mission
• Partnership between NASA, NGA, DLR and ASI
• C-band single-pass interferometry
• X-band interferometer by German and Italian space agencies
• Mission duration: Feb., 11 – Feb. 22, 2000 (11 days, 5 hours)
Objective
• Produce DEM of 80% of Earth surface
! 30 meter posting
! Complete coverage ± 60° latitude
! WGS84 EGM96 geoid datum
! 16 m vert., 20 m horiz. accuracy at 90% level
4. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Topographic Coverage
• DTED1 coverage
• 90 meter samples
• ~ 60% coverage
• Poor continuity
• SRTM coverage
• 30 meter samples
• ~ 80% coverage
• Excellent continuity
5. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Accuracy results
Accuracy tests
• NGA verification with DTED, other data sources
• USGS comparisons to National Elevations Data sets, GPS arrays
• Comparison to static and dynamic GPS elevations
• Scores of open literature reports comparing to local data sets
All show data exceed 16 m vertical accuracy specs by factor of ~3
Elevations clearly show patterns of tropical forest
clear-cutting with height differences of 4-5 meters
6. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Data distributed by Land Processes DAAC at EDC
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7. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Data Restrictions
Re original NASA/NGA Memorandum of Understanding, DEMs better than 3
arcsec outside U.S territory may not be distributed outside NASA
3 arcsec DEMs and any derived, ancillary or associated products may be
distributed at any resolution, as long as they cannot be reverse-engineered to
better than 3 arcsec DEMs
8. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
NASA MEaSUREs Program
Under MEaSUREs NASA has funded effort to create SRTM-derived,
enhanced, merged global data set
! • Void filled DEMs
! • Enhanced DEM control using ICESat profiles
! • Individual and mosaiced radar image data
! • Raw radar and ancillary data set
9. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
NASA MEaSUREs Program
NASA has funded effort to create SRTM-derived, enhanced, merged
global data set
! • Void filled DEMs
! • Enhanced DEM control using ICESat profiles
! • Individual and mosaiced radar voids - cell average 0.52%, median 0.0023%
SRTM image data
! • Raw radar and ancillary data set
10. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Many SRTM DEMs have voids
• Voids (areas with no data) caused
by high slopes or extremely low SNR
• Can be filled with supplementary DEMs
SRTM voids - cell average 0.52%, median 0.0023%
Original SRTM Voids filled with ASTER DEM
11. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Potential Void Fill Sources
NED
!
• National Elevation Data (NED), 30m
- Excellent quality
- Available only for U.S., Mexico
• Canadian Digital Elevation Data (CDED), ~ 20m
- Available for virtually all of Canada CDED
• Other sources - various resolutions
- SRTM / X-SAR
- TanDEM-X
- SPOT
- Australian GEODATA Topo
X-SAR
• ASTER GDEM
- Excellent coverage
- Matches SRTM geoid, format, sampling
- Some voids and artifacts
12. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
GDEM
With NASA support Silcast Corp. processed several million ASTER
stereo pairs to produce a global DEM
GDEM1 released in mid-2009
- Matches SRTM in posting, datum, formatting
- Coverage exceeds SRTM
- Resolution ~ 120m, versus 50-60m for SRTM
- Numerous artifacts due to cloud mask, stacking method
GDEM2 to be released in mid-2011
- Resolution improved by using smaller convolution kernel
- Many artifacts remain
13. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
GDEM coverage
SRTM coverage
14. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
GDEM2 - SRTM
Global Land
Cover
EGM96
15. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Comparison
SRTM ASTER GDEM
GDEM1
16. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Comparison
SRTM GDEM1
17. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
SRTM w/ void GDEM1
18. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
SRTM GDEM1
19. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
SRTM ASTER GDEM1
20. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
GDEM includes ancillary NUM file showing number
of pairs contributing to each sample
SRTM GDEM1 GDEM Num
Capetown area, South Africa
21. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
GDEM2 Improvement
GDEM1: Original version delivered mid-2009
- Suffered from low resolution (~120 m), cloud and data stacking induced artifacts
GDEM2: Beta version being evaluated now
- Improved resolution with smaller convolution kernel, many artifacts remain
- Included quality files allow identification of most(?) artifacts
SRTM GDEM1 GDEM2 GDEM2 N>2
22. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
NUM = 3
• Voiding samples with NUM < 3 seems to
eliminate most artifacts
• Deletes ~ 7% of GDEM samples
• GDEM2 and SRTM voids only loosely
correlated
Distribution of NUMs in GDEM2
23. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Voids
SRTM GDEM2 N>2
SRTM/GDEM2 Combo
24. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
NASA MEaSUREs Program
NASA has funded effort to create SRTM-derived, enhanced, merged
global data set
! • Void filled DEMs
! • Enhanced DEM control using ICESat profiles
! • Individual and mosaiced radar image data
! • Raw radar and ancillary data set
25. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
ICESat Data
• Comparisons of ICESat profiles w/ SRTM data have revealed possible bias at
latitude extremes
• Other published work suggests possible SRTM bias in high elevation
Malaspina
glaciated terrain (this deserves more attention)
• SRTM ocean residuals (should center on zero) show curious latitude
dependence
• Residuals are well within accuracy specs, but systematic nature is obvious
• Important to study of glacier changes with implications for climate change
SRTM ocean residuals from unedited data
Residual latitude dependence
26. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
ICESat Data
• M. Simard working with Carabajal, Sauber et al at GSFC to develop database of ICESat
topographic profiles for additional ground truth
• Requires careful modeling of laser echoes, differentiation of vegetation, bare Earth
returns
• Will be applied as additional ground control for void-filled DEMs
• Processing using new control data to begin mid-2011
27. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
‘Bonus Data’
• Rasterized version of SRTM Water Body Data (significant improvement to
World Vector Shoreline) - complete, ~15000 ESDRs to be shipped Aug., 2011
28. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Void-filling Summary
• Plan as proposed: Void fill SRTM on best effort basis (~20% of tiles)
• GDEM1 available: Void fill much of SRTM with manual artifact detection
• GDEM2 available: Void fill most of SRTM with semi-automated artifact
detection
• Will allow data generation greatly exceeding original MEaSUREs plan
and
• Expanded plan...
29. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Expanded Plan
• Original NASA-NGA MOU has expired, creating possibility of distributable full-
resolution data set
• At NASA request generated set of options for SRTM data “reprocessing”
• A task plan for an expanded effort is now under review at NASA Hq
• Plan will involve additional resources and an additional year beyond end of
MEaSUREs program in 2012
• Resulting data set to be called NASADEM
30. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Elements of NASADEM
• First step is interferometric reprocessing of SRTM raw data with improved algorithms, faster
computers
- Better handling of Beam Adaptive Tracker and other ancillary data
- Improved phase unwapping algorithms
- Leverage NASA ʻCloudʼ computing resources
• ICESat database will provide vastly improved ground control
• GDEM2 quality incorporating NUM files is sufficient to merge (not just void-fill) with SRTM,
most voids will disappear
• Remaining voids will be filled with new ASTER acquisitions or other data sets
- EDC has agreed to provide additional ASTER DEMs as required from archive or new
acquisitions
- R. Crippen has developed technique for detecting and eliminating artifacts in individual
ASTER DEMs
• SRTM water mask will allow incorporation of NGA coastline and other water body information
31. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Raw radar echoes NASADEM ASTER GDEM2
Artifact removal
SAR processing (updated)
Current SRTM Modified GDEM2
Global DEM
Updated strip DEMs
Void-fill
SRTM
Metric Data
Continental Adjustment (mostly) void- Remaining
IceSAT filled SRTM (ver3) void map
Data Set
Acquire new
ʻNewʼ SRTM Global DEM NED, CDED, et al LPDAAC ASTER DEMs
New ASTER DEMs
SRTM Water
Body Data
Modified DEMs Artifact removal
Merge corrected DEMs
Key:
Pink: New data set or process NASADEM + associated products:
Yellow: Existing data set or process
Slope, Aspect, Curvature, Images,
Process
Errors, Correlation
32. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Elements of NASADEM
• Data will be distributed and freely available at full 30
meter posting!
• New data types included
- Correlation maps
- Error metrics
- Terrain corrected images, slopes, et al
• Processing will be continent-by-continent, with release
throughout latter half of 2013
33. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
NASA MEaSUREs Program
NASA has funded effort to create SRTM-derived, enhanced, merged
50.02a
global data set 151.07d
! • Void filled DEMs
! • Enhanced DEM control using ICESat profiles
! • Individual and mosaiced radar image data
! • Raw radar and ancillary data set
34. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Radar Image Task Status
Basic Image Set
• Includes image, incidence angle map for each subswath crossing each cell
• Subswath inclusion allows differentiating polarizations
• Numerous files/cell, total data set ~ 700 gigabytes (compressed!)
• Distributable at 1 arcsec
• Complete: > 365,000 ESDRs en route to Land Processes DAAC
Data take number (may be from 2 to ~20)
34.02a 50.02a 135.07d 151.07d
Images
Individual data take (per subswath)
stacks for each cell
incidence
angles
35. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
SRTM Image Data Example Application
Image Incidence angle
User-defined
region of interest
Scatter plot generates backscatter curve
36. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Radar Image Task Status
Central Canada
Single-swath mosaic
Average of all swaths
37. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
Radar Image Task Status
GILF KEBIR
TM SRTM
38. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
SRTM Radar Image of Lake Baikal
39. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology
40. A Merged Global Digital Jet Propulsion Laboratory
Topographic Data Set California Institute of Technology