Quanti-litative Revolution in GIS

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Talk about How Big Data can help in the new GIS world. The talk goes from the old GIS days to nowadays usage of geodata and gives some insight on the future using Distributed Technologies and ad hoc analyses.

@Noootsab
Pre-NextLab
Licence in Maths, ULg
Licence in Computer Sciences, ULg
Geospatial specialization, ULg
Many years in GIS (Ionic Software and its mutations)
Always in data management and semantic
New technologies enthusiast
NextLab age
Project in for the SPW
Project in for Virdata
Wrote a about Play! 2
evangelist (OKFN, ...), speaker
Distributed + Distributed expert
co-founder | organizer
And the list goes on... and on...

Raster
vulgus: Big Fat
MultiBand Images
SPOT (see later)
IKONOS (greek: image)
LANDSAT
ALOS
QuickBird: resolution 64cm
→ 2m44cm
Vector
vulgus... nah, vector
Feature extraction
Landmeters (rare)
Field surveying
~GPS (GNS)

thus, Users were
Govs!
Oh yeah, researchers as well... huh!? wait!
Okay, very feeeew privates

in consequence, Analyses were
Performed by experimented folks...
... or nerds

finally, Tools were...
You don't even know them!
ESRI, Oracle Spatial,
OGC, RedSpider (yeah), ...

Qualitative GIS
"Started" around 2000
Actually, not really before 2010

GIS Revolution
Induce theories from data
The river has flooded on 3km
The river will flood if it rains more than...

New producers
Google, 4², OSM, GPS
old: Mappy, Michelin, ...

Analyses
Routing, Accurate GWR¹, ...
Disaster prediction, flood prevention, crash probability, ...
¹Geospatial Weight Regression

Tools?
User friendly and/or Open Source
OSM
GeoServer
OpenLayers
Google Maps

Quantilitative GIS
Coined by @Noootsab^^

Rasters today (f.i. Spot7)
Red Green Blue + Near Infra Red Bands
One shot: 60x60km (3.600 sq km)
Takes 3.600.000 sq km of geodata per DAY
Resolution 2 satellites at and 2 others at
Revolution: 110 minutes
26 days to complete the geoid (all pieces of crap covered)
1 single f*****g file for a 60x60km tile is worth up to

Vector (mostly position)
everywhere and everytime
Twitter, Facebook, ...
Foursquare, Instagram
Waze
Google (in its whole)
Connected Devices

Presidente
Model-Driven
Deductive
Top-Down
Quantitative
Lagged Time
GIS Putsch
Commandante
Data-Driven
Inductive
Bottom-Up
Qualitative
Real Time
Marcelino
Data Lake
Machine Learning
Variety
Value
Velocity
VOLUME? It was there for ages!

Who, What, How
You thought I was joking, huh!

Socrata
Evan Chan using Spark
Customers have point data (many millions of
rows)
PostGIS: point-in-polygon and other does
Partitioning point data in (tiling, Z-curve)
Partitions into for quick analysis
Adding to Spark for speeding up

Azavea
Rob Emanuele on Geotrellis
Run of climate data against daily
temperature and precipitation data out to 2009
Create suitability maps over high-res raster layers
spanning the for Urban Forestry
Modeling.
GeoTrellis is providing with geospatial capabilities.
Ingest, mosaic and pyramid raster data into
for fast (sub-500ms) tile fetching

Snips
Rand Hindi in his Labs
Tranquilien: seating availability in public
transport
RiskContext: Determining the risk of bicycle and car
Using technologies that can scale linearly
(Akka, Scala)
Thanks to the bottom up of the architecture, the
clusters keeps crunching data in a resilient manner

Done!
Thx & cu on Twitter
@noootsab
@NextLab_be

This document discusses tools and projects for digitizing and georeferencing historical maps on the web in an open and collaborative manner. It presents examples like Gutenkarte for browsing geographic texts, and the NYPL Map Rectifier and Digitizer. It also covers best practices for public editing, versioning, and resolving disputes on collaborative projects. Finally, it promotes open access to historical maps and building a shared database through crowd-sourcing with tools like Whooms and Mapwarper.

Satellite eyes observe our world: An intro to remote sensing

Dr. Harald Schernthanner

Remote sensing uses sensors on satellites and aircraft to observe and analyze areas from a distance without direct contact. The document discusses the history of remote sensing from early color photography to modern high-resolution satellites. It also describes applications like mapping and monitoring land use and permafrost. Finally, it provides a tutorial on deriving vegetation indices from Sentinel-2 satellite data using free and open-source software.

Software's for amateur astronomy

Antilen Jacob

This document discusses various types of software for amateur astronomy, including planetarium programs like Stellarium to view celestial objects, simulators like Celestia to simulate space environments, image processing programs like AIPS to analyze astronomical images, mathematics programs like NumPy for calculations, and programs like wmMoonClock that track the sun and moon. It provides examples of specific software under each category to aid amateur astronomers.

Keeping a Sentinel Eye on the Volcanoes – from Space!

Advanced-Concepts-Team

Sentinel-1 satellites, ESA’s Synthetic Aperture Radar (SAR) mission, provide continuous data from the Earth surface in weekly to biweekly time intervals. This data availability provides an unprecedented opportunity to continuously monitor the Earth surface motion in areas prone to geohazards; such as regions of high seismic and volcanic activities, with the end goal of supporting the Early Warning Systems. However, the great challenge is to derive insights from Terabytes of satellite image sequences, in a computationally-efficient and time-critical manner. We’ve risen to this challenge by designing innovative signal processing and deep learning algorithms to efficiently mine this invaluable wealth of data. This talk gives on overview of our designed solutions, as well as a demonstration of these solutions in the Tectonic and Volcanic monitoring of South America (TecVolSA) project.

Landsat data and its application in landuse and landcover .(NIT ROURKELA)

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Surveying the Trends in Geospatial Data: From Pixels to Point Clouds

Safe Software

This document discusses trends in geospatial data from the 1960s to present. It describes the evolution of data from early raster datasets to today's large point clouds. Key developments discussed include the rise of CAD/GIS in the 1980s, spatial databases in the 1990s, web/cloud in the 2000s, 3D/BIM in the 2000s, and the explosion of point cloud data in the 2010s. The document emphasizes that more data in more formats enables better spatial analysis and decision making.

This document summarizes a BA Computing Studies degree with a major in Cartography from the CCAE (Central Computer Applications Establishment). The core disciplines of the degree are Cartography, Mathematics, and Computer Science. Course topics include datums and coordinate systems, terrain modelling, computer programming, numerical analysis, and machine intelligence. An example assignment from a Remote Sensing course is described, requiring the processing of LANDSAT imagery to extract and map Lake Burley Griffin and its features in Canberra. The document also discusses early concepts for automated object and feature recognition from maps and imagery to fulfill queries, as well as the development of interactive mapping software called WIMS for the Joint Intelligence Organisation.

Landsat 8 OLI (Operational Land Imager)

Haris Khan

Landsat 8 is a satellite that collects images of Earth's surface using two sensors - the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). OLI collects images in 9 bands at a spatial resolution of 30 meters for most bands and 15 meters for the panchromatic band. TIRS collects thermal data at 100 meter spatial resolution. Landsat 8 has a 16 day temporal resolution and collects data globally with a 185km swath width. It provides improved capabilities compared to previous Landsat satellites such as enhanced coastal and cirrus cloud detection bands.

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Yi Lin

This document provides an introduction to working with maps and geographic information systems (GIS). It discusses different types of map data like points, lines and polygons, as well as common coordinate systems. It also introduces several popular map APIs and tools for visualizing spatial data, including Google Maps, Mapbox and OpenStreetMap. Examples are given of using maps for applications like navigation, location tracking and statistical analysis of regional data. Questions about working with maps and spatial data are welcomed.

Sentinel Satellites

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TexelTek - Andrew Levine - Hadoop World 2010

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The document discusses using an open cloud consortium to process map imagery for disaster relief. It aims to make imagery available online for relief workers, enable large-scale image processing of satellite data, and provide image deltas showing changes over time. The framework uses Apache Hadoop on a testbed platform to break images into tiles via mappers and assemble them from reducers into layers for a web map service. It demonstrates change detection over time for disasters like oil spills and floods.

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American Astronautical Society

LANDSAT

Sumant Diwakar

The Landsat program is the longest running satellite program for imaging Earth. It began in 1972 with the launch of Landsat 1 and has continued with subsequent launches of Landsat satellites every few years. Landsat satellites acquire millions of images with spectral bands at resolutions from 15-60 meters to support research in global change, agriculture, geology, forestry and other areas. Landsat 7, launched in 1999, still operates today despite an instrument failure, providing images every 16 days.

Torusbern

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Satellite systems landsat 7 compatibility mode

Sabaragamuwa University of Sri Lanka

The document summarizes information about satellite systems, with a focus on the Landsat program. It discusses that Landsat 1 was launched in 1972 and started the Landsat program for collecting remote sensing data of Earth. Landsat 7 is the most recent satellite, launched in 1999, and it collects global images with improved technology to refresh the satellite image archive. The document also provides details on the sensors and applications of Landsat 7 for monitoring Earth's environment, geology, natural resources, and land use from its sun-synchronous polar orbit.

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Landsat was a joint NASA/USGS satellite program designed to systematically acquire global land surface images. Landsat 1 was launched in 1972 as the first satellite dedicated to observing Earth's land areas. Subsequent Landsat satellites carried improved sensors with higher spatial, spectral, and radiometric resolutions. Landsat provides repetitive coverage of the entire global land mass with images useful for mapping and monitoring land use change over time.

Band Combination of Landsat 8 Earth-observing Satellite Images

Kabir Uddin

This document discusses Landsat 8, an Earth-observing satellite launched by NASA in 2013. It provides details on Landsat 8 such as its mission to continuously archive global images of Earth since the 1970s, how it collects about 400 scenes per day, and its various spectral bands that can be used alone or combined for different analyses. Band combination examples are shown for Landsat 8 images before and after processing of Inle Lake to demonstrate the use of natural color, color infrared, and different false color composites.

GOOGLE EARTH INTRODUCTION

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Google Maps began as a keyhole.com project funded by the CIA to make satellite imagery accessible. It was acquired by Google and developed to include street maps, business listings, and navigation functions accessible on computers and mobile devices. Google Maps has expanded to include specialized versions like Google Sky, Google Mars, and marine mapping, using data from space agencies, oceanographers, and third party sources. It remains one of Google's most popular and widely used products.

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Landsat program

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Sentinel 2

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The document provides information about accessing and using data from the Sentinel-2 satellite mission. It outlines several portals for downloading Sentinel-2 imagery, including the ESA SciHub, Amazon Web Services, and USGS websites. It describes how to search for and filter imagery using criteria like date, cloud cover percentage, and region. The document also defines some key terms related to Sentinel-2 imagery like granules, datastrips, and datatakes. It lists the spatial resolutions of the different spectral bands and gives examples of pre-processing steps that can be applied to the data like resampling, atmospheric correction, and classification.

Spot 7 satellite

Nimra Butt

SPOT is a commercial Earth observation satellite system operated by Spot Image. It began in the 1970s with SPOT 1 launched in 1986, and continues today with SPOT 6 and SPOT 7 launched in 2012 and 2014 respectively. The SPOT satellites provide high-resolution optical imagery to map the Earth's surface and monitor human activities and natural phenomena. SPOT 7 carries multispectral sensors and has a resolution of 1.5 meters for panchromatic imagery and 6 meters for multispectral, allowing it to map wide areas quickly and be used for applications like weather prediction, urban mapping, and land use analysis.

Intelligent satellites

Clifford Stone

1. The document describes the concept for a future generation of intelligent Earth observing satellites that would provide real-time satellite imagery and data to end users. 2. Key elements of the proposed system include a network of low Earth orbit satellites linked to geostationary satellites, with on-board processing and high-speed data transmission to allow direct downlinking of imagery and data to users. 3. The system is designed around user needs, with components like handheld receivers and mobile antennas allowing real-time access to satellite data, as well as user software to process and display the data.

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RJW CCAE Course 1977-79

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Landsat 8 OLI (Operational Land Imager)

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Sentinel Satellites

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