Difference between revisions of "20180813-knoxville-foss4g"
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==Sponsor== | ==Sponsor== | ||
| − | + | *Michael Hamrick | |
| + | *OSGEO.us | ||
| + | *Civil Engineering Designs, LLC | ||
*North River Geographic Systems, Inc. | *North River Geographic Systems, Inc. | ||
*University of Tennessee Department of Geography | *University of Tennessee Department of Geography | ||
| Line 33: | Line 35: | ||
: Randal Hale | : Randal Hale | ||
: NRGS | : NRGS | ||
| + | : Youtube: https://www.youtube.com/watch?v=62eG9H4wrEE&t= | ||
: QGIS is a open source desktop which has now been rewritten for python 3 and QT5. With that re-write it's more than ever a possible replacement for Commercial GIS desktops. I'll run through some of the major changes and where I think QGIS is headed. | : QGIS is a open source desktop which has now been rewritten for python 3 and QT5. With that re-write it's more than ever a possible replacement for Commercial GIS desktops. I'll run through some of the major changes and where I think QGIS is headed. | ||
| Line 39: | Line 42: | ||
: Dr. Hannah C. Gunderman | : Dr. Hannah C. Gunderman | ||
: Advanced Short-Term Research Opportunities Program Participant at Oak Ridge National Laboratory | : Advanced Short-Term Research Opportunities Program Participant at Oak Ridge National Laboratory | ||
| + | : Youtube: https://www.youtube.com/watch?v=jJAqFFEOmgo | ||
: Although Africa has experienced notable successes in technology, healthcare, and transportation development, there remain several factors that threaten the stability of several countries within the continent. The role that geopolitics, human development, and environmental health play on population stability is significant. This research will demonstrate a methodology for providing a scenario-based population forecast for each of the 54 countries in Africa based on the data fusion of several geopolitical, environmental, and social metrics. Conducted 100% through Python in PyCharm, African countries are categorized on a 1-5 scale of scenario-based population stability for the next five years based on considerations of their population structure, their Human Development Index, Fragile States Index, and Environmental Performance Index scores, and if the country is experiencing geopolitical unrest. The resulting visualization, made exclusively in QGIS, allows geopoliticians, humanitarian researchers, and data scientists to devote increased attention toward predicted crisis areas on the continent. | : Although Africa has experienced notable successes in technology, healthcare, and transportation development, there remain several factors that threaten the stability of several countries within the continent. The role that geopolitics, human development, and environmental health play on population stability is significant. This research will demonstrate a methodology for providing a scenario-based population forecast for each of the 54 countries in Africa based on the data fusion of several geopolitical, environmental, and social metrics. Conducted 100% through Python in PyCharm, African countries are categorized on a 1-5 scale of scenario-based population stability for the next five years based on considerations of their population structure, their Human Development Index, Fragile States Index, and Environmental Performance Index scores, and if the country is experiencing geopolitical unrest. The resulting visualization, made exclusively in QGIS, allows geopoliticians, humanitarian researchers, and data scientists to devote increased attention toward predicted crisis areas on the continent. | ||
| − | '''Open | + | '''"Implementing a Discrete Global Grid System (DGGS); a New Open Geospatial Consortium (OGC) Standard." ''' |
| − | : | + | : Dr Paulo Raposo |
| − | : | + | : UTK |
| − | : | + | : Youtube: https://www.youtube.com/watch?v=SY0bJSF_F5M&t=2s |
| − | : | + | |
| + | : We'll be talking about DGGS, what they are, how they're new at the OGC, and how we used FOSS software to implement one. | ||
'''Landscape Level LiDAR and the National Land Cover Dataset''' | '''Landscape Level LiDAR and the National Land Cover Dataset''' | ||
: Doug Newcombe | : Doug Newcombe | ||
| + | : Youtube: https://www.youtube.com/watch?v=PKjhizrXSuw | ||
: Multiple LiDAR collections from Southeast Virginia analyzed for canopy height, relative vegetation density at 1-3m and 3-7m heights aggregated at 30m resolution aligned with the US National Land Cover Dataset (NLCD), 10m resolution nested inside the 30m cells and at 6.096 m. Forest/non - forest classes at 5m heights where compared to forest and shrub classifications present in the NLCD. | : Multiple LiDAR collections from Southeast Virginia analyzed for canopy height, relative vegetation density at 1-3m and 3-7m heights aggregated at 30m resolution aligned with the US National Land Cover Dataset (NLCD), 10m resolution nested inside the 30m cells and at 6.096 m. Forest/non - forest classes at 5m heights where compared to forest and shrub classifications present in the NLCD. | ||
| Line 56: | Line 62: | ||
: Paul Dudley | : Paul Dudley | ||
: State of Tennessee | : State of Tennessee | ||
| + | : Youtube: https://www.youtube.com/watch?v=jEefMPq4Q-Q | ||
: The State of Tennessee has an amazing project underway to collect statewide Lidar data in a partnership with the USGS 3D Elevation Program. This data is in the public domain and is easily downloaded. Presentation will cover project specifics, available data products, access and common data use cases. | : The State of Tennessee has an amazing project underway to collect statewide Lidar data in a partnership with the USGS 3D Elevation Program. This data is in the public domain and is easily downloaded. Presentation will cover project specifics, available data products, access and common data use cases. | ||
| + | |||
| + | '''Implementing PostgreSQL and PostGIS in a water utility''' | ||
| + | :Julian Burke | ||
| + | :Chatsworth Water Works Commission | ||
| + | :CWWC moved their GIS data collection to a PostgreSQL database and eliminated shapefiles and File Based Geodatabases. They are now able to combine other data stored in databases with their new system. | ||
'''Understanding patterns of vegetation structure and distribution across Great Smoky Mountains National Park using LiDAR and meteorology data''' | '''Understanding patterns of vegetation structure and distribution across Great Smoky Mountains National Park using LiDAR and meteorology data''' | ||
: Jitendra Kumar | : Jitendra Kumar | ||
| + | : Youtube: https://www.youtube.com/watch?v=Xk-owg02j-4 | ||
: Great Smoky Mountains National Park (GSMNP) in Tennessee is a biodiversity hotspot and home to a large number of plant, animal and bird species. Driven by gradients of climate (ex. temperature, precipitation regimes), topography (ex. elevation, slope, aspect), geology (ex. soil types, textures, depth), hydrology (ex. drainage, moisture availability) etc. GSMNP offers a diverse composition and distribution of vegetation which in turn supports an array of wildlife. Understanding the vegetation canopy structure is critical to understand, monitor and manage the complex forest ecosystems like the Great Smoky Mountain National Park (GSMNP). Vegetation canopies not only help understand the vegetation, but are also a critically important habitat characteristics of many threatened and endangered animal and bird species that GSMNP is home to. Using airborne Light Detection and Ranging (LiDAR) we characterize the three-dimensional structure of the vegetation. LiDAR based analysis gives detailed insight in the canopy structure (overstory and understory) and its spatial variability within and across forest types. Vegetation structure and spatial distribution show strong correlation with climate, topographic, and edaphic variables and our multivariate analysis not just mines rich and large LiDAR data but presents ecological insights and data for vegetation within the park that can be useful to forest managers in their management and conservation efforts. | : Great Smoky Mountains National Park (GSMNP) in Tennessee is a biodiversity hotspot and home to a large number of plant, animal and bird species. Driven by gradients of climate (ex. temperature, precipitation regimes), topography (ex. elevation, slope, aspect), geology (ex. soil types, textures, depth), hydrology (ex. drainage, moisture availability) etc. GSMNP offers a diverse composition and distribution of vegetation which in turn supports an array of wildlife. Understanding the vegetation canopy structure is critical to understand, monitor and manage the complex forest ecosystems like the Great Smoky Mountain National Park (GSMNP). Vegetation canopies not only help understand the vegetation, but are also a critically important habitat characteristics of many threatened and endangered animal and bird species that GSMNP is home to. Using airborne Light Detection and Ranging (LiDAR) we characterize the three-dimensional structure of the vegetation. LiDAR based analysis gives detailed insight in the canopy structure (overstory and understory) and its spatial variability within and across forest types. Vegetation structure and spatial distribution show strong correlation with climate, topographic, and edaphic variables and our multivariate analysis not just mines rich and large LiDAR data but presents ecological insights and data for vegetation within the park that can be useful to forest managers in their management and conservation efforts. | ||
| Line 66: | Line 79: | ||
'''Convolutional Neural Network Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion''' | '''Convolutional Neural Network Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion''' | ||
: Forrest M Hoffman | : Forrest M Hoffman | ||
| + | : Youtube: https://www.youtube.com/watch?v=fDIXaFCKqBc | ||
: Accurate and high-resolution maps of vegetation are critical for projects seeking to understand terrestrial ecosystem processes and land–atmosphere interactions in Arctic ecosystems, such as the U.S. Department of Energy’s Next Generation Ecosystem Experiment (NGEE) Arctic. However, most existing Arctic vegetation maps are at a coarse resolution and with a varying degree of detail and accuracy. Remote sensing-based approaches for mapping vegetation, while promising, are challenging in high latitude environments due to frequent cloud cover, polar darkness, and limited availability of high-resolution remote sensing datasets (e.g., 5 m). This study proposes a new remote sensing-based multi-sensor data fusion approach for developing high-resolution maps of vegetation in the Seward Peninsula, Alaska. We focused the detailed analysis and validation study around the Kougarok River, located in the central Seward Peninsula of Alaska. | : Accurate and high-resolution maps of vegetation are critical for projects seeking to understand terrestrial ecosystem processes and land–atmosphere interactions in Arctic ecosystems, such as the U.S. Department of Energy’s Next Generation Ecosystem Experiment (NGEE) Arctic. However, most existing Arctic vegetation maps are at a coarse resolution and with a varying degree of detail and accuracy. Remote sensing-based approaches for mapping vegetation, while promising, are challenging in high latitude environments due to frequent cloud cover, polar darkness, and limited availability of high-resolution remote sensing datasets (e.g., 5 m). This study proposes a new remote sensing-based multi-sensor data fusion approach for developing high-resolution maps of vegetation in the Seward Peninsula, Alaska. We focused the detailed analysis and validation study around the Kougarok River, located in the central Seward Peninsula of Alaska. | ||
| Line 71: | Line 85: | ||
'''Hey, where are these spatial indexes?: Adventures of PostGIS in Production''' | '''Hey, where are these spatial indexes?: Adventures of PostGIS in Production''' | ||
: Sean Brewer | : Sean Brewer | ||
| + | : Youtube: https://www.youtube.com/watch?v=LHmZ17946Yk | ||
: When building database-backed software, it's in your best interest to be smart about using your database. Postgres and PostGIS are very powerful. Often though, useful features get ignored or not used in the right way. Did you remember to put indexes on your geometry columns? Did you know you can generate and manipulate JSON/GeoJSON inside PostGIS? Did you know PostGIS is capable of analysis and geoprocessing inside the database? I will show you techniques that helped our PostGIS backed software to quickly serve over 10 million requests a month at our peak. | : When building database-backed software, it's in your best interest to be smart about using your database. Postgres and PostGIS are very powerful. Often though, useful features get ignored or not used in the right way. Did you remember to put indexes on your geometry columns? Did you know you can generate and manipulate JSON/GeoJSON inside PostGIS? Did you know PostGIS is capable of analysis and geoprocessing inside the database? I will show you techniques that helped our PostGIS backed software to quickly serve over 10 million requests a month at our peak. | ||
Latest revision as of 06:29, 17 September 2018
Detail
FOSS4Gx Knoxville 2018
8/13/2018 - 8:30am to 4:30pm
2505 EJ Chapman Dr
Knoxville, TN 37996
Join us for the second annual FOSS4Gx Knoxville 2018. This day conference is an opportunity for local Free and Open Source GIS users to get together and share stories, discuss projects, software, and get to know one another. This event is volunteer run and organized so if we are missing something, please let us know and if you can help us out.
The ticket price to attend is $10. The money collected from tickets and from any sponsorship will defray expenses for this event. Any profit generated from this event will be used to fund future workshops, trainings, or conferences. If the money is an issue with you attending please contact Randy and we will get it handled.
Please join our mailing list to get the latest updates about the event.
Registration
Register for our event now while tickets are still available. There has been a lot of interest in this event and we are limited to the number of seats in the room, so please don't wait and end up missing out.
Sponsor
- Michael Hamrick
- OSGEO.us
- Civil Engineering Designs, LLC
- North River Geographic Systems, Inc.
- University of Tennessee Department of Geography
- UT County Technical Assistance Service
Thank you to our generous sponsors for participating in this event.
We are looking for sponsors to help keep the cost of attendance low so if you or your company are interested in sponsoring or donating to the cause please contact Randy.
Presentation
What's new in QGIS 3.x
- Randal Hale
- NRGS
- Youtube: https://www.youtube.com/watch?v=62eG9H4wrEE&t=
- QGIS is a open source desktop which has now been rewritten for python 3 and QT5. With that re-write it's more than ever a possible replacement for Commercial GIS desktops. I'll run through some of the major changes and where I think QGIS is headed.
Fusing Geopolitics, Human Development, and Environmental Policy Data to Predict Population Stability through Open Source Software: A Case Study Across Africa
- Dr. Hannah C. Gunderman
- Advanced Short-Term Research Opportunities Program Participant at Oak Ridge National Laboratory
- Youtube: https://www.youtube.com/watch?v=jJAqFFEOmgo
- Although Africa has experienced notable successes in technology, healthcare, and transportation development, there remain several factors that threaten the stability of several countries within the continent. The role that geopolitics, human development, and environmental health play on population stability is significant. This research will demonstrate a methodology for providing a scenario-based population forecast for each of the 54 countries in Africa based on the data fusion of several geopolitical, environmental, and social metrics. Conducted 100% through Python in PyCharm, African countries are categorized on a 1-5 scale of scenario-based population stability for the next five years based on considerations of their population structure, their Human Development Index, Fragile States Index, and Environmental Performance Index scores, and if the country is experiencing geopolitical unrest. The resulting visualization, made exclusively in QGIS, allows geopoliticians, humanitarian researchers, and data scientists to devote increased attention toward predicted crisis areas on the continent.
"Implementing a Discrete Global Grid System (DGGS); a New Open Geospatial Consortium (OGC) Standard."
- Dr Paulo Raposo
- UTK
- Youtube: https://www.youtube.com/watch?v=SY0bJSF_F5M&t=2s
- We'll be talking about DGGS, what they are, how they're new at the OGC, and how we used FOSS software to implement one.
Landscape Level LiDAR and the National Land Cover Dataset
- Doug Newcombe
- Youtube: https://www.youtube.com/watch?v=PKjhizrXSuw
- Multiple LiDAR collections from Southeast Virginia analyzed for canopy height, relative vegetation density at 1-3m and 3-7m heights aggregated at 30m resolution aligned with the US National Land Cover Dataset (NLCD), 10m resolution nested inside the 30m cells and at 6.096 m. Forest/non - forest classes at 5m heights where compared to forest and shrub classifications present in the NLCD.
“Tennessee’s Public Lidar Data”
- Paul Dudley
- State of Tennessee
- Youtube: https://www.youtube.com/watch?v=jEefMPq4Q-Q
- The State of Tennessee has an amazing project underway to collect statewide Lidar data in a partnership with the USGS 3D Elevation Program. This data is in the public domain and is easily downloaded. Presentation will cover project specifics, available data products, access and common data use cases.
Implementing PostgreSQL and PostGIS in a water utility
- Julian Burke
- Chatsworth Water Works Commission
- CWWC moved their GIS data collection to a PostgreSQL database and eliminated shapefiles and File Based Geodatabases. They are now able to combine other data stored in databases with their new system.
Understanding patterns of vegetation structure and distribution across Great Smoky Mountains National Park using LiDAR and meteorology data
- Jitendra Kumar
- Youtube: https://www.youtube.com/watch?v=Xk-owg02j-4
- Great Smoky Mountains National Park (GSMNP) in Tennessee is a biodiversity hotspot and home to a large number of plant, animal and bird species. Driven by gradients of climate (ex. temperature, precipitation regimes), topography (ex. elevation, slope, aspect), geology (ex. soil types, textures, depth), hydrology (ex. drainage, moisture availability) etc. GSMNP offers a diverse composition and distribution of vegetation which in turn supports an array of wildlife. Understanding the vegetation canopy structure is critical to understand, monitor and manage the complex forest ecosystems like the Great Smoky Mountain National Park (GSMNP). Vegetation canopies not only help understand the vegetation, but are also a critically important habitat characteristics of many threatened and endangered animal and bird species that GSMNP is home to. Using airborne Light Detection and Ranging (LiDAR) we characterize the three-dimensional structure of the vegetation. LiDAR based analysis gives detailed insight in the canopy structure (overstory and understory) and its spatial variability within and across forest types. Vegetation structure and spatial distribution show strong correlation with climate, topographic, and edaphic variables and our multivariate analysis not just mines rich and large LiDAR data but presents ecological insights and data for vegetation within the park that can be useful to forest managers in their management and conservation efforts.
Convolutional Neural Network Approach for Mapping Arctic Vegetation using Multi-Sensor Remote Sensing Fusion
- Forrest M Hoffman
- Youtube: https://www.youtube.com/watch?v=fDIXaFCKqBc
- Accurate and high-resolution maps of vegetation are critical for projects seeking to understand terrestrial ecosystem processes and land–atmosphere interactions in Arctic ecosystems, such as the U.S. Department of Energy’s Next Generation Ecosystem Experiment (NGEE) Arctic. However, most existing Arctic vegetation maps are at a coarse resolution and with a varying degree of detail and accuracy. Remote sensing-based approaches for mapping vegetation, while promising, are challenging in high latitude environments due to frequent cloud cover, polar darkness, and limited availability of high-resolution remote sensing datasets (e.g., 5 m). This study proposes a new remote sensing-based multi-sensor data fusion approach for developing high-resolution maps of vegetation in the Seward Peninsula, Alaska. We focused the detailed analysis and validation study around the Kougarok River, located in the central Seward Peninsula of Alaska.
Hey, where are these spatial indexes?: Adventures of PostGIS in Production
- Sean Brewer
- Youtube: https://www.youtube.com/watch?v=LHmZ17946Yk
- When building database-backed software, it's in your best interest to be smart about using your database. Postgres and PostGIS are very powerful. Often though, useful features get ignored or not used in the right way. Did you remember to put indexes on your geometry columns? Did you know you can generate and manipulate JSON/GeoJSON inside PostGIS? Did you know PostGIS is capable of analysis and geoprocessing inside the database? I will show you techniques that helped our PostGIS backed software to quickly serve over 10 million requests a month at our peak.
Submit
If you want to submit a presentation please contact Randy and the committee will get back with you. Talks will be 20 minutes with 5 minutes for questions. Presentations will be posted as they are accepted.