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VECTOR DATA PROCESSING USING PYTHON TOOLS 10:30: Introduction: 10:31: Geospatial Concepts: What is ‘vector’ geospatial data all about? 10:36 GOOGLE EARTH ENGINE: HOME 09:00: GEE Access and JavaScript Tips: How do I get an account? What are some JavaScript basics? 09:00 MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A ML as AI. Machine learning is a component of artificial intelligence (AI) (a broader subject). There are many subject areas where ML may be applied, e.g. sound, language, and vision (essentially, traits we can identify with as a human). MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Prerequisites. Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. RASTER PROCESSING USING PYTHON TOOLS 10:30: Setup Tutorial Python Environment: How do I setup a Python environment to run examples in this tutorial? 10:35 GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Objectives. Use reducers to aggregate a daily image collection to annual values. Import vector data to summarize values by polygon regions. Use climate data products available through GEE RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Objectives. Understand the raster data model. Describe the strengths and weaknesses of storing data in raster format. Distinguish between continuous and categorical raster data and identify types of datasets that would be stored in each format. RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS AND Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
GEOHACKWEEK 2019
ABOUT GEOHACKWEEK. Geohackweek is a 5-day hackweek to be held at the University of Washington eScience Institute. Participants will learn about open source technologies used to GOOGLE EARTH ENGINE: HOME 09:00: GEE Access and JavaScript Tips: How do I get an account? What are some JavaScript basics? 09:00 GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Spatial data is unique because plots containing it (i.e., maps) have specialized axes.Think about it: looking at your favorite projection, the gridlines aren’t necessarily straight, nor are they evenly-spaced, like they would be if we drew lines onto the plot above.. For this reason, we employ cartopy to define the mapping between our data and our visualization. VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Advanced topics. Emilio Mayorga, University of Washington. 2019-9-8. We covered the basics of GeoPandas in the previous episode and notebook. Here, we’ll extend that introduction to illustrate additional aspects of GeoPandas and its interactions with other Python libraries, covering fancier mapping, reprojection, analysis (unitary and binary spatial operators), raster zonal GITHUB PROJECT MANAGEMENT Edit me GitHub Tools. All participants have been added to the geohackweek organization page before arriving. GitHub organizations are shared accounts allowing people to collaborate across many projects at once. Once projects are formed participants will be organized into teams.Teams are convenient because we can set repository permissions to groups of people rather than individuals,and in our
VECTOR DATA PROCESSING USING PYTHON TOOLS: REFERENCE Useful References. Most of these links are blog posts. The date shown is the blog post date, or else some other document date. A couple of links are in French, all from the same blog; apologies, but they’re excellent resources – if you know French! RASTER PROCESSING USING PYTHON TOOLS 10:30: Setup Tutorial Python Environment: How do I setup a Python environment to run examples in this tutorial? 10:35 GOOGLE EARTH ENGINE: ACCESSING SATELLITE IMAGERY Overview: Satellite Imagery at Regional Scales. Most satellite products are broken up into tiles for distribution. Global Landsat data is broken up in ~180 km 2 scenes, with unique path/row identifiers. 455 scenes cover the United States. Each scene is currently imaged every 16 days by Landsat 8, and every 16 days by Landsat 7 (approximately 45 times each year). RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Objectives. Understand the raster data model. Describe the strengths and weaknesses of storing data in raster format. Distinguish between continuous and categorical raster data and identify types of datasets that would be stored in each format. GOOGLE EARTH ENGINE: TIME SERIES Overview. This code allows users to dynamically generate time series plots for from points that are dynamically chosen on a map on the fly. The time series show the 16 day composites of Normalized Difference Vegetation Index and Enhanced Vegetation Index at 250 m resolution. VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS AND Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS AND Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
GEOHACKWEEK 2019
Geohackweek is a 5-day hackweek to be held at the University of Washington eScience Institute. Participants will learn about open source technologies used to analyze geospatial datasets. Mornings will consist of interactive lectures, and afternoon sessions will involve facilitated exploration of datasets and hands-on software development. RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
VECTOR DATA PROCESSING USING PYTHON TOOLS: REFERENCE Vector Data Processing using Python Tools. : Reference. A common terminology and set of concepts has been defined by the OGC Simple Feature Access, and is widely used across open source geospatial libraries. Core geometric objects (point, line/linestring and polygon) and their multi-part collections (multi-point, multi-line,multi-polygon) are
GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GITHUB PROJECT MANAGEMENT Edit me GitHub Tools. All participants have been added to the geohackweek organization page before arriving. GitHub organizations are shared accounts allowing people to collaborate across many projects at once. Once projects are formed participants will be organized into teams.Teams are convenient because we can set repository permissions to groups of people rather than individuals,and in our
GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be GOOGLE EARTH ENGINE: ACCESSING SATELLITE IMAGERY Overview: Satellite Imagery at Regional Scales. Most satellite products are broken up into tiles for distribution. Global Landsat data is broken up in ~180 km 2 scenes, with unique path/row identifiers. 455 scenes cover the United States. Each scene is currently imaged every 16 days by Landsat 8, and every 16 days by Landsat 7 (approximately 45 times each year). GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the timeseries.
INTRODUCTORY MATERIAL: INTRODUCTION TO CONDA To know the current environment that you’re in you can either look at your terminal: (test_env) D-69-91-135-15:env_files lsetiawan$. The (test_env) in the beginning of the line indicates that I’m curently using the test_env conda environment. Another way that you can check for your current active environment is a command: $ conda env list. GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Spatial data is unique because plots containing it (i.e., maps) have specialized axes.Think about it: looking at your favorite projection, the gridlines aren’t necessarily straight, nor are they evenly-spaced, like they would be if we drew lines onto the plot above.. For this reason, we employ cartopy to define the mapping between our data and our visualization. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A ML as AI. Machine learning is a component of artificial intelligence (AI) (a broader subject). There are many subject areas where ML may be applied, e.g. sound, language, and vision (essentially, traits we can identify with as a human). VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Pandas + geometry data type + custom geo goodness. Emilio Mayorga, University of Washington. 2019-9-8. Background. GeoPandas adds a spatial geometry data type to Pandas and enables spatial operations on these types, using shapely.GeoPandas leverages Pandas together with several core open source geospatial packages and practices to provide a uniquely GOOGLE EARTH ENGINE: HOME 09:00: GEE Access and JavaScript Tips: How do I get an account? What are some JavaScript basics? 09:00 GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Prerequisites. Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Objectives. Use reducers to aggregate a daily image collection to annual values. Import vector data to summarize values by polygon regions. Use climate data products available through GEE GOOGLE EARTH ENGINE: TIME SERIES Overview. This code allows users to dynamically generate time series plots for from points that are dynamically chosen on a map on the fly. The time series show the 16 day composites of Normalized Difference Vegetation Index and Enhanced Vegetation Index at 250 m resolution. RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS AND Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Spatial data is unique because plots containing it (i.e., maps) have specialized axes.Think about it: looking at your favorite projection, the gridlines aren’t necessarily straight, nor are they evenly-spaced, like they would be if we drew lines onto the plot above.. For this reason, we employ cartopy to define the mapping between our data and our visualization. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A ML as AI. Machine learning is a component of artificial intelligence (AI) (a broader subject). There are many subject areas where ML may be applied, e.g. sound, language, and vision (essentially, traits we can identify with as a human). VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Pandas + geometry data type + custom geo goodness. Emilio Mayorga, University of Washington. 2019-9-8. Background. GeoPandas adds a spatial geometry data type to Pandas and enables spatial operations on these types, using shapely.GeoPandas leverages Pandas together with several core open source geospatial packages and practices to provide a uniquely GOOGLE EARTH ENGINE: HOME 09:00: GEE Access and JavaScript Tips: How do I get an account? What are some JavaScript basics? 09:00 GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Prerequisites. Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Objectives. Use reducers to aggregate a daily image collection to annual values. Import vector data to summarize values by polygon regions. Use climate data products available through GEE GOOGLE EARTH ENGINE: TIME SERIES Overview. This code allows users to dynamically generate time series plots for from points that are dynamically chosen on a map on the fly. The time series show the 16 day composites of Normalized Difference Vegetation Index and Enhanced Vegetation Index at 250 m resolution. RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS AND Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
GEOHACKWEEK 2019
ABOUT GEOHACKWEEK. Geohackweek is a 5-day hackweek to be held at the University of Washington eScience Institute. Participants will learn about open source technologies used to VECTOR DATA PROCESSING USING PYTHON TOOLS 10:30: Introduction: 10:31: Geospatial Concepts: What is ‘vector’ geospatial data all about? 10:36 VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Advanced topics. Emilio Mayorga, University of Washington. 2019-9-8. We covered the basics of GeoPandas in the previous episode and notebook. Here, we’ll extend that introduction to illustrate additional aspects of GeoPandas and its interactions with other Python libraries, covering fancier mapping, reprojection, analysis (unitary and binary spatial operators), raster zonal GOOGLE EARTH ENGINE: HOME 09:00: GEE Access and JavaScript Tips: How do I get an account? What are some JavaScript basics? 09:00 VECTOR DATA PROCESSING USING PYTHON TOOLS: REFERENCE Useful References. Most of these links are blog posts. The date shown is the blog post date, or else some other document date. A couple of links are in French, all from the same blog; apologies, but they’re excellent resources – if you know French! RASTER PROCESSING USING PYTHON TOOLS 10:30: Setup Tutorial Python Environment: How do I setup a Python environment to run examples in this tutorial? 10:35 MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A ML as AI. Machine learning is a component of artificial intelligence (AI) (a broader subject). There are many subject areas where ML may be applied, e.g. sound, language, and vision (essentially, traits we can identify with as a human). GOOGLE EARTH ENGINE: ACCESSING SATELLITE IMAGERY Overview: Satellite Imagery at Regional Scales. Most satellite products are broken up into tiles for distribution. Global Landsat data is broken up in ~180 km 2 scenes, with unique path/row identifiers. 455 scenes cover the United States. Each scene is currently imaged every 16 days by Landsat 8, and every 16 days by Landsat 7 (approximately 45 times each year). RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Objectives. Understand the raster data model. Describe the strengths and weaknesses of storing data in raster format. Distinguish between continuous and categorical raster data and identify types of datasets that would be stored in each format. GOOGLE EARTH ENGINE: PYTHON API: 02 FEATURE AND RASTER DATA title: “Feature & Raster Data” teaching: 45 exercises: 15 questions: - “What is the difference between feature and raster data in Earth Engine?” - “How do I load, visualize and filter feature and image data in GEE?” objectives: - “Load datasets and successfully visualize in the GEE API” - “Filter an image collection” - “Know how to share code and manage versions” VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the timeseries.
VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODSSEE MORE ON GEOHACKWEEK.GITHUB.IO VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IO RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image. GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the timeseries.
VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
VECTOR DATA PROCESSING USING PYTHON TOOLS: REFERENCE Vector Data Processing using Python Tools. : Reference. A common terminology and set of concepts has been defined by the OGC Simple Feature Access, and is widely used across open source geospatial libraries. Core geometric objects (point, line/linestring and polygon) and their multi-part collections (multi-point, multi-line,multi-polygon) are
MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GOOGLE EARTH ENGINE: REFERENCE Google Earth Engine is a powerful tool for analyzing remote sensing imagery. GEE’s data catalog hosts a formidable variety of earth observation data. GEE is changing how we understand and study the Earth. Code Editor. The Code Editor is a one stop shop for accessing GEE data catalog and conducting geospatial analysis. GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be GOOGLE EARTH ENGINE: ACCESSING SATELLITE IMAGERY Overview: Satellite Imagery at Regional Scales. Most satellite products are broken up into tiles for distribution. Global Landsat data is broken up in ~180 km 2 scenes, with unique path/row identifiers. 455 scenes cover the United States. Each scene is currently imaged every 16 days by Landsat 8, and every 16 days by Landsat 7 (approximately 45 times each year). GOOGLE EARTH ENGINE: TIME SERIES Add the time series plots to the panels. Now that we have set up our user interface and built the call-back, we can define a time series chart. The chart uses the lat/long selected by the user and builds a time series for NDVI or EVI at that point. It takes the average NDVI or EVI at that point, extracts it, and then adds it to the timeseries.
INTRODUCTORY MATERIAL: INTRODUCTION TO CONDA To know the current environment that you’re in you can either look at your terminal: (test_env) D-69-91-135-15:env_files lsetiawan$. The (test_env) in the beginning of the line indicates that I’m curently using the test_env conda environment. Another way that you can check for your current active environment is a command: $ conda env list. VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Pandas + geometry data type + custom geo goodness. Emilio Mayorga, University of Washington. 2019-9-8. Background. GeoPandas adds a spatial geometry data type to Pandas and enables spatial operations on these types, using shapely.GeoPandas leverages Pandas together with several core open source geospatial packages and practices to provide a uniquely RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS ANDGDAL FOR PYTHONGDAL TRANSLATE PYTHON EXAMPLEPYTHON GDAL APIPYTHON GDAL COOKBOOKPYTHON GDAL DRIVER Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IOADVANTAGES OF SUPERVISED CLASSIFICATIONSUPERVISED CLASSIFICATION ALGORITHMS GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection: VECTOR DATA PROCESSING USING PYTHON TOOLS 11:15. GeoPandas Advanced Topics. What additional capabilities does GeoPandas provide, including data access, plotting and analysis? How does it integrate with other common Python tools? How do GeoPandas data objects integrate with analyses of raster data over vector geospatial features? 11:50. OpenStreetMap data access and processing. VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Pandas + geometry data type + custom geo goodness. Emilio Mayorga, University of Washington. 2019-9-8. Background. GeoPandas adds a spatial geometry data type to Pandas and enables spatial operations on these types, using shapely.GeoPandas leverages Pandas together with several core open source geospatial packages and practices to provide a uniquely RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GOOGLE EARTH ENGINE: GEE ACCESS AND JAVASCRIPT TIPS Please complete this tutorial before arriving at geohackweek. Use the steps below to get registered for a Google Earth Engine account and to join our shared repository. 1. Registering for a Google Earth Engine account. Go the the GEE sign up page and enter > the email you want to use for your GEE account. A gmail is best if you have one. GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be RASTER PROCESSING USING PYTHON TOOLS: RASTER FORMATS ANDGDAL FOR PYTHONGDAL TRANSLATE PYTHON EXAMPLEPYTHON GDAL APIPYTHON GDAL COOKBOOKPYTHON GDAL DRIVER Libraries and file formats for raster datasets. GDAL (Geospatial Data Abstraction Library) is the de facto standard library for interaction and manipulation of geospatial raster data. The primary purpose of GDAL or a GDAL-enabled library is to read, write and transform geospatial datasets in a way that makes sense in the context of itsspatial metadata.
GOOGLE EARTH ENGINE: SUPERVISED CLASSIFICATION OFSEE MORE ON GEOHACKWEEK.GITHUB.IOADVANTAGES OF SUPERVISED CLASSIFICATIONSUPERVISED CLASSIFICATION ALGORITHMS GOOGLE EARTH ENGINE: TEMPORAL AND SPATIAL REDUCERS Reducers: Overview. In Google Earth Engine (GEE), reducers are used to aggregate data over time, space, and other data structures. They belong to the ee.Reducer class and include summary statistics, histograms, and linear regression, among others. Here’s a visual from Google demonstrating a reducer applied to an ImageCollection:GEOHACKWEEK 2019
Geohackweek is a 5-day hackweek to be held at the University of Washington eScience Institute. Participants will learn about open source technologies used to analyze geospatial datasets. Mornings will consist of interactive lectures, and afternoon sessions will involve facilitated exploration of datasets and hands-on software development. GEOSPATIAL DATA VISUALIZATION The purpose of this tutorial is to 1) foster a working knowledge of basic geospatial visualization tools in Python and 2) expose participants to the wide landscape of spatial visualization tools, both programmatic (using code, e.g. cartopy) and graphical (by clicking, e.g. Tableau, ArcGIS).It is based on the lesson template used in Data Carpentry and Software Carpentry workshops and was VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS Advanced vector geospatial analytics. Go over all the analytics that have been wrapped into GeoPandas, including Geometric Manipulations, Set-Operations with Overlay, Aggregation with Dissolve and Merging Data. Take a look at PySAL, the Python Spatial Analysis Library. It’s in the conda environment. This is a powerful, multi-facetedpackage.
RASTER PROCESSING USING PYTHON TOOLS Raster Formats and Libraries. What sorts of formats are available for representing raster datasets? 10:55. Working with Raster Datasets. How can I extract pixel values from rasters and perform computations? How might I write pixel values out to a new raster file? 11:40. Rainier DEM Example. How can I work with rasters from different sources VECTOR DATA PROCESSING USING PYTHON TOOLS: GEOPANDAS GeoPandas: Pandas + geometry data type + custom geo goodness. Emilio Mayorga, University of Washington. 2019-9-8. Background. GeoPandas adds a spatial geometry data type to Pandas and enables spatial operations on these types, using shapely.GeoPandas leverages Pandas together with several core open source geospatial packages and practices to provide a uniquely GOOGLE EARTH ENGINE: HOME Introduction to Google Earth Engine. What is Google Earth Engine? What are the strengths and limitations of this platform? 09:05. Code Editor. What are the key features of the online code editor? Where can I go for help while learning GEE? How do I search for and import GEOSPATIAL DATA VISUALIZATION: BASICS: QUICK + SIMPLE MAPS Cartopy 101. For our purposes here, cartopy is a python package which provides a set of tools for creating projection-aware geospatial plots using python’s standard plotting package, matplotlib. Cartopy also has a robust set of tools for defining projections and reprojecting data, which are used under-the-hood in our tutorial, but won’t be MACHINE LEARNING: SUPERVISED LEARNING: TREE-BASED METHODS Tree boosting methods. Beyond random forests, there are other alterations to tree-based machine learning models that have improved accuracy and other nice properties. We’ll focus on gradient boosting. Because trees are discrete and add special complications, let’s understand boosting conceptually using generic functions andregression.
MACHINE LEARNING: MACHINE LEARNING WITH GIS APPLICATION (A Machine learning is a general term used to apply to many techniques which utilize statistical iteration and feedback so that correlations or logic is learned rather than dictated. Learning itself is the act of gradually improving performance on a task without being explicitly programmed. This process mimics human neurological functions. RASTER PROCESSING USING PYTHON TOOLS: INTRODUCTION TO Raster data is any pixelated (or gridded) data where each pixel is associated with a specific geographical location. The value of a pixel can be continuous (e.g. elevation) or categorical (e.g. land use). If this sounds familiar, it is because this data structure is very common: it’s how we represent any digital image.GEOHACKWEEK 2019
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GEOHACKWEEK 2019
Workshop On Geospatial Data Science University of Washington eScience InstituteSept 9 - 13, 2019
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ABOUT GEOHACKWEEK
Geohackweek is a 5-day hackweek to be held at the University of Washington eScience Institute. Participants will learn about open source technologies used to analyze geospatial datasets. Mornings will consist of interactive lectures, and afternoon sessions will involve facilitated exploration of datasets and hands-on software development. See 2018 Geohackweek. | Learnabout "hackweek".
INFORMATION FOR APPLICANTS To best benefit from the program, participants are expected to have some experience with Python programming and with analysis of geospatial data (e.g. remote sensing analysis, vector mapping, environmental modeling, etc.). Successful applicants will pay a $100 registration fee and be expected to cover lodging and travel expenses. Financial support may be available based on need.OUR SPONSORS
Thanks to our sponsors that make this event possible.LOCATION
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Room 221, 1601 NE Columbia Rd, Seattle, WA 98195×
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