The document discusses the integration of big data analytics with remote sensing and GIS, emphasizing the five V's of big data: volume, velocity, value, veracity, and variety. It highlights the importance of Earth observation data in monitoring natural hazards and climate change, alongside advancements in data collection and analysis methods. Additionally, it introduces tools like Google Earth Engine for conducting planetary-scale geospatial analysis, leveraging extensive satellite data.

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Remote Sensing Big Data Analytics with GIS

2. The Five V’s
2
Scale of Data
Analysis of
Data Flow
Structured and
Unstructured Data
Uncertainty
of Data
Big
Data
Volume
Velocity
Value
Veracity
Variety

3. Big Remote Sensing Data

4. Earth Observation Satellites

5. Need of Earth Observation
5
Natural Hazard Monitoring,
Global Climate Change
Soil Conservation
Volcanic Phenomenon
Water Pollution

6. Earth Observation Data
6
Source: https://commons.wikimedia.org/w/index.php?
search=earth+observation+data&title=Special:MediaSearch&go=Go&type=image

7. Features of Remote-sensing Big Data
7
Big
Data
Volume
Velocity
Value
Veracity
Variety
Collect
Manage
Store
Archive
Analyse
Visualize
Distribute
Ground, Aerial,
Satellite, UAV
Large achieve,
Real time
Optical, Microwave,
Hyperspectral, LiDAR
Data Enhance Through
Visualization

8. Features of Remote-sensing Big Data
8
Quick Bird II: Resolution.: 0.61 meter panchromatic
2.4 meter multispectral
Source:
https://www.satimagingcorp.com/gallery/quickbir
d/quickbird-umbrella-cay-bahamas/ Source:
https://nara.getarchive.net/media/ert-landsat-satellite-and-lake
michigan-a8eb2b
Landsat 8 images have 15-meter panchromatic and 30-meter
multi-spectral spatial resolutions along a 185 km (115 mi) swath.

9. The Dynamic state of Remote Sensing Big Data
9

10. Raster Data/ Vector Data
10

11. What is GIS?
11
What is GIS?: “A computer - assisted
system for the capture, storage
retrieval, analysis and display of spatial
data, within a particular Organization”.
(Clarke, 1986)
A GIS is a computer-based system that
provides the four sets of capabilities to
handle geo-referenced data: GIS
Input
Data
Management
Output
Manipulation
and Analysis

12. How GIS Used
12
 Identify Problems
 Monitor Change
 Manage and Respond to Events
 Perform forecasting
 Set priorities
 Understand trends

13. Planetary-scale Geospatial Analysis
13
Google Earth Engine: Next Generation Digital Earth
“Big Data,” paradigm of science that emphasizes international collaboration,
data-intensive analysis, huge computing resources, and high-end
visualization.”
Source: https://docs.google.com/presentation/d/1hT9q6kWigM1MM3p7IEcvNQlpPvkedW-lgCCrIqbNeis/edit#slide=id.g4e98c855a5_0_0
33 years Of satellite data
Over 5,000,000 Landsat and
Sentinel scenes analysed
3 Quadrillion Pixels
(3,000,000,000,000,000)

14. Planetary-scale Geospatial Analysis
14
Google Earth Engine: Next Generation Digital Earth
Source: https://docs.google.com/presentation/d/1hT9q6kWigM1MM3p7IEcvNQlpPvkedW-lgCCrIqbNeis/edit#slide=id.g4e98c855a5_0_0

15. Planetary-scale Geospatial Analysis
15
Google Earth Engine: Next Generation Digital Earth
Source: https://docs.google.com/presentation/d/1hT9q6kWigM1MM3p7IEcvNQlpPvkedW-lgCCrIqbNeis/edit#slide=id.g4e98c855a5_0_0

16. Planetary-scale Geospatial Analysis
16
Google Earth Engine: Next Generation Digital Earth
Source: https://docs.google.com/presentation/d/1hT9q6kWigM1MM3p7IEcvNQlpPvkedW-lgCCrIqbNeis/edit#slide=id.g4e98c855a5_0_0

17. Planetary-scale Geospatial Analysis
17
Google Earth Engine coder
Source: https://docs.google.com/presentation/d/1hT9q6kWigM1MM3p7IEcvNQlpPvkedW-lgCCrIqbNeis/edit#slide=id.g4e98c855a5_0_0
https://code.earthengine.google.com/

18. Future of big data analytics in remote sensing and GIS?
18

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