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Erdas 9.2,arcgis & Matlab Work Videos about practical of ERDAS ARCGIS & MATLAB

15/07/2018

What is image classification? Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. Depending on the interaction between the analyst and the computer during classifi...

15/07/2018

Get familiar with using Python code to perform image processing methods and algorithms – and what they mean

http://forestry.usu.edu/videos-conferences-webinars/webinars/webinars
11/04/2017

http://forestry.usu.edu/videos-conferences-webinars/webinars/webinars

In this presentation, I will review the results of a global study that reviews the literature on vegetation and human health, and then maps the potential for trees to help more than 200 major cities globally. Urban vegetation already plays a small but important role in reducing particulate matter co...

http://joshconsult.com/2017/04/05/stories-showing-strength-gis/
05/04/2017

http://joshconsult.com/2017/04/05/stories-showing-strength-gis/

Stories Showing the Strength of GIS GIS has come a long way since Roger Tomlinson introduced the term ‘geographic information system’ in his paper titled ‘A Geographic Information System for Regional Planning’. Even so, today’s geographic information systems still generate and maintain spatial infor...

30/03/2017

He is doing amazing! Just watch this great tutorial!
Very clear and comprehensive understanding. Simplified as well.
HIGHLY RECOMMENDED!
http://videolectures.net/roks2013_shawe_taylor_kernels/

Kernels can be viewed as shallow in that learning is only applied in a single (output) layer. Recent successes with deeper networks highlight the need to consider richer function classes. The talk will review and discuss methods that have been developed to enable richer kernel classes to be learned....

Application of Remote Sensing and GIS in Geological Mapping & Mineral Exploration,.(case study of El-Beida Area, Eastern...
17/03/2017

Application of Remote Sensing and GIS in Geological Mapping & Mineral Exploration,.
(case study of El-Beida Area, Eastern
Desert of Egypt)
Part I : Landsat-8 OLI Band Description
Part II : Landsat-8 OLI Preprocessing
Part III : Landsat-8 OLI Image processing
Part IV: Aster data Description & processing
Part V: Sentinel-2 data description & processing
Comparison between the 3 Sensor false color band composites
Part VI : Lithological Mapping
Part VII : Hydrothermal Alteration
Part VIII : Lineaments Extraction
Part IX : Identifying Favorable Target Areas for Field Exploration
https://www.academia.edu/31903449/Application_of_Remote_Sensing_and_GIS_in_Geological_Mapping_and_Mineral_Exploration._case_study_of_El-Beida_Area_Eastern_Desert_of_Egypt

This study used the ability of remote sensing technology to identify and map the lithological units and alteration zones in a gold prospect area in El-beida El-kobra, South Eastern Desert of Egypt by using Landsat 8 Oli, Aster L1t and Sentinel-2 data

13/03/2017

& :
1. Determining soil moisture content using active and passive sensors from space
2. Mapping with laser precision using Light Detection and Ranging technology
3. Catching tax-evaders red-handed by locating new construction and building alterations
4. Spinning the globe with mapping services like Google Earth, Bing Maps and OpenStreetMaps
5. Predicting retail earnings and market share by counting cars in parking lot
6. Snapping aerial photos for military surveillance using messenger pigeons in World War II
7. Charging higher insurance premiums in flood-prone areas using radar
8. Doing the detective work for fraudulent crop insurance claims
9. Searching for aircrafts and saving lives after fatal crashes
10. Detecting oil spills for marine life and environmental preservation
11. Counting polar bears to ensure sustainable population levels
12. Uncovering habitat suitability and fragmentation for panda bears in protected areas
13. Identifying forest stands and tallying their area to estimate forest supplies
14. Navigating ships safely with the most optimal route
15. Measuring wind speed and direction for wind farms, weather forecasting and surfers
16. Spying on enemies with reconnaissance satellites
17. Delineating and assessing the health of riparian zones to conserve lakes and rivers
18. Estimating surface elevation with the Shuttle Radar Topography Mission
19. Extracting mineral deposits with hyperspectral remote sensing
20. Watching algae grow as an indicator of environmental health
21. Forecasting weather to warn about natural disasters
22. Detecting land cover/use types for decision making
23. Monitoring the environment with the ESA’s Copernicus Program
24. Mapping soil types for agriculture planning
25. Preventing the spread of forest disease types
26. Fighting wildfires by planning firefighter dispatch
27. Monitoring air quality in the lower atmosphere
28. Assessing terrain stability using interferometry in the oil and gas sector
29. Unearthing ancient archaeological sites like the Mayans and ancient Egypt
30. Pinpointing your position on Earth with Global Positioning Satellites
31. Optimizing solar panel energy output with global horizontal irradiance
32. Finding the driving factors that contribute to poverty
33. Observing the flow of ocean currents and circulation
34. Studying glacier melts and effects on sea levels
35. Providing a basemap for visual reference and assisting orient the map reader
36. Snorkeling in an oasis of marine vegetation with the coastal channel
37. Tracking hazards for better response and recovery
38. Keeping tabs on the shift from rural to urban growth
39. Quantifying crop conditions with Normalized Difference Vegetation Index (NDVI)
40. Preventing the degradation and loss of wetland ecosystems
41. Tracking sediment transport into rivers and lakes
42. Saving money and time on the farm with precision farming
43. Reversing illegal rainforest cutting in Brazil
44. Putting illegal boat dumping under the microscope
45. Inventorying and assessing rural road conditions with UAVs
46. Driving with no hands (autonomous vehicles)
47. Measuring gravity with the GRACE satellites
48. Deriving elevation and contours using photogrammetry
49. Watching the aurora borealis from another angle
50. Comparing the past and present with human impact change
51. Planning an optimal telecom network capacity
52. Tracking displaced refugees to help deliver aid and services
53. Covering the most ground in search of road cracks
54. Getting a top-down view when purchasing real estate
55. Keeping a watchful eye to prevent future atrocities from happening
56. Designing a lift irrigation system to supply water in India
57. Measuring the volume difference at a uranium enrichment site using 3d mapping
58. Helping provide clean drinking water with basemaps
59. Monitoring active volcanoes using thermal remote sensing
60. Inventorying potential landslides with interferometry
61. Catching fish and improving long-term fisheries sustainability
62. Tracking the great distances of migratory birds and inspecting their prevalence
63. Preventing the spread of diseases in epidemiology
64. Recording video footage from satellites
65. Quantifying the damage after an earthquake
66. Looking at the Earth as an art masterpiece
67. Recognizing buildings easily with the bird’s eye oblique view
68. Mapping the mysteries of our ocean floors
69. Understanding the human rights situation in North Korea
70. Comparing climatic factors from past to present
71. Monitoring the global s*x trade situation in remote areas
72. Assessing fuel economy of vehicle emissions
73. Providing early warning signs for famine over large scales
74. Mapping regional economic activity at night
75. Studying geology of the Earth’s surface
76. Assessing the environmental change and promoting biodiversity in parks
77. Measuring albedo for Earth’s radiation budget
78. Locating groundwater activity for wells
79. Observing population growth in urban areas using land use change
80. Keeping a watchful eye on biodiversity
81. Keeping an inventory on cemeteries using UAVs
82. Predicting the occurrence of dinosaur tracks for paleontologists
83. Delineating watersheds using DEMs for hydrologists
84. Using habitat suitability models to predict the abundance of mosquitoes
85. Using a least-cost analysis and vegetation to understand wildebeest migration
86. Assisting cities manage assets and ensuring safety standards
87. Calculating the depth of snowpack
88. Planning spine-jarring black diamond ski runs with aspect data
89. Improving efficiency and safety of air traffic control
90. Spotting undeclared nuclear power plants automatically
91. Narrowing down a search for a missing body
92. Monitoring oil reserves by looking at floating oil roof tanks
93. Finding ghost cities on the map
94. Spotting swimming pools for late-night dives
95. Reducing traffic jams using change detection
96. Measuring the size of protests for journalists
97. Measuring the rise of sea levels
98. Creating an automated road network instantly
99. Picking up on signals from submarines in shallow water
100. Exploring, protecting and navigating in the arcti

06/03/2017

Here’s the full, sortable list of the 50 most promising startups from around the world.

02/03/2017

Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping
http://journal.frontiersin.org/article/10.3389/feart.2017.00017/full

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “...

01/03/2017

Hey Guys,
Another interesting paper has been published in European Remote Sensing Journal latest issue
Object-based water body extraction model using Sentinel-2 satellite imagery
Water body extraction is an important part of water resource management and has been the topic of a number of research works related to remote sensing for over two decades. Extracting water bodies from satellite images with a pixel-based method or indexes cannot eliminate other objects that have a low albedo, such as shadows and built-up areas.
Thanks for this interesting study Gorde Daniela Kaplan Uğur Avdan
Paper is freely available athttp://www.tandfonline.com/doi/full/10.1080/22797254.2017.1297540

28/02/2017

A new MOOC on Ecosystem Services produced by the University of Geneva, the Geneva Water Hub, the Luc Hoffmann Institute, and the Natural Capital Project is now available.
This MOOC is for anybody interested in mastering the strengths and weaknesses of the Ecosystem Services concept as a tool for promoting sustainable development. Subjects covered are both technical (methods for valuation, data acquisition, etc.) and socio-political (how to mainstream the process, ethics, criticism of the method, etc.). Watch an introductory video (3min) to get a feel for the course content and philosophy.
Learners will hear from many of the field’s leading minds. The course is taught by three primary instructors and by 29 guest instructors and interviewees coming from many of the key institutions (the Natural Capital Project, WWF, IUCN, IPBES, TEEB, Luc Hoffmann Institute) and Universities. Learners can expect to hear multiple contrasting opinions. See the full syllabus and list of instructors on the course page.
This course is offered “on-demand”. In practice, class cohorts are formed on a regular basis and you can take as much time as necessary to complete the course (2-5 hours for 5 weeks is a rough estimate). The first cohort begins February 2nd 2017 and once per month thereafter.
Access to all course materials and exams is free.
To obtain a certificate of completion costs 49 USD. Financial aid is available (see FAQs on bottom of the course page).
https://www.coursera.org/learn/ecosystem-services

27/02/2017

Jobs and employment opportunities at Plan-It Geo

Comparison of Spatial Resolutions in Satellite Images
27/02/2017

Comparison of Spatial Resolutions in Satellite Images

I wanted to better understand what is the difference between different spatial resolutions in satellite images. What can you really see in…

27/02/2017

Hey Guys,
Before asking for the remote sensing tools/softwares or programme, please check the link below:

Or Ask google first as it will provide you some great links for downloading, sometimes freely available ones. We, shared this link several times with you.
Also please note that we are not your "e-book finder". You may search on your library databases and can download it.
http://www.grss-ieee.org/open-source-software-related-to-geoscience-and-remote-sensing/

25/02/2017

Understanding Earth Observation: The Electromagnetic Foundation of Remote Sensing Author: Domenico Solimini This volume addresses the physical foundation of remote sensing. The basic grounds are... Read more

Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Ser...
23/02/2017

Effect of Training Class Label Noise on Classification Performances for Land Cover Mapping with Satellite Image Time Series
Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise.
http://www.mdpi.com/2072-4292/9/2/173/htm

23/02/2017

Remote sensing platforms and sensors: A survey
The objective of this article is to review the state-of-the-art remote sensing technologies, including platforms and sensors, the topics representing the primary research interest in the ISPRS Technical Commission I activities. Due to ever advancing technologies, the remote sensing field is experiencing unprecedented developments recently, fueled by sensor advancements and continuously increasing information infrastructure. The scope and performance potential of sensors in terms of spatial, spectral and temporal sensing abilities have expanded far beyond the traditional boundaries of remote sensing, resulting in significantly better observation capabilities. First, platform developments are reviewed with the main focus on emerging new remote sensing satellite constellations and UAS (Unmanned Aerial System) platforms. Next, sensor georeferencing and supporting navigation infrastructure, an enabling technology for remote sensing, are discussed. Finally, we group sensors based on their spatial, spectral and temporal characteristics, and classify them by their platform deployment competencies. In addition, we identify current trends, including the convergence between the remote sensing and navigation field, and the emergence of cooperative sensing, and the potential of crowdsensing.
http://www.sciencedirect.com/science/article/pii/S0924271615002270

The objective of this article is to review the state-of-the-art remote sensing technologies, including platforms and sensors, the topics representing the primary research interest in the ISPRS Technical Commission I activities. Due to ever advancing technologies, the remote sensing field is experien...

A State-of-the-Art Review on the Integration of Building Information Modeling (BIM) and Geographic Information System (G...
23/02/2017

A State-of-the-Art Review on the Integration of Building Information Modeling (BIM) and Geographic Information System (GIS)
The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration.
http://www.mdpi.com/2220-9964/6/2/53

The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologi...

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