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ArcGIS Viewshed keeps crashing

ArcGIS Viewshed keeps crashing


I've been trying to run a viewshed analysis in ArcGIS 10.1 and everytime I've tried running it, it crashes the whole program, with no error. See the image below.

I've made sure it has OFFSETA field in right format, that it is one point only in the shapefile, and also that 3D analyst is ticked on.

Additionally I have also tried to run it as a Python script but it still produced the same result.

The elevation and XY distances are the same unit, in metres, though I haven't specified metres in the OFFSETA column. My input layers are a DSM raster that is quite big (85000x85000 roughly, 27.23 GB), and a point layer that is one point only. They are both in British National Grid projection. Im using ArcGIS 10.1 SP 1


I was just having this exact same problem. The whole ArcMap program would crash as soon as I started to run Viewshed and no error was returned. I tried the tip offered by @PolyGeo to try a smaller dataset, so I clipped my DEM to a smaller extent and ran the tool again with the new, smaller raster. This time it immediately worked without a problem. (yay!)

One related question to the comment offered by @radouxju: In many of the threads I came across trying to solve this problem, someone suggests making sure that the elevation (Z) and XY distances are in the same unit, but as far as I can tell, this isn't an issue if you use the Z factor to compensate for different units. My DEM had XY units in meters and Z units in feet, but I entered a factor of 0.3048 to let the tool convert units on the fly.


I have two suggestions:(1) Place your inputs in a folder on your desktop. The point is to have a short folder path. Your inputs will be a raster DEM and a point or polyline shapefile. (2) Avoid using numbers or capital letters when naming your output (the viewshed). This has worked for me so far.


ArcGIS Viewshed keeps crashing - Geographic Information Systems

Environmental Science, Policy & Engineering Program

Winter 2022

GEOGRAPHIC INFORMATION SYSTEMS

Professor Dr. Ashraf Ghaly, P.E.
Department Engineering
Office Olin 102D
Tel., email 518-388-6515, [email protected]

Lectures: TTH 9:05AM-10:50AM, Wold-028. Labs: choose one on either T or TH 2:25PM-5:15PM, Wold-028. (Click HERE for instructor's class presentations)

An introduction to Geographic Information Systems (GIS) technology and its practical uses. A full range of fundamental topics will be covered including the history of GIS, technology overview, geographic data types, primary data structures, system design, map coordinate systems, data sources, metadata, census data, geographic coding and address matching, digitizing, remote sensing imagery, measures of data quality, and needs assessment. An emphasis will be on hands-on instruction using GIS software (ArcGIS). Students will work with ArcGIS throughout the term to complete assignments and a class project. Focus areas include archaeology, electric and gas utilities, surveying, health and human services, insurance, law enforcement and criminal justice, media and telecommunications, transportation, water and wastewater, and natural resources. The ultimate goal is to use the spatial component of data in conducting analysis and making decisions. Two class hours and two lab hours weekly. Prerequisites: A good background in the use of modern computer software.

  • Assignments & Quizes = 20%
  • Labs = 20%
  • Mid Term Test (6th week) = 20%
  • Project GIST = 20%
  • Final Examination = 20%
  • Assigned homework is due as will be arranged. Late submission results in partial grade loss. Each day of late submission results in 2 points loss (of ten).
  • Attendance of labs is mandatory.
  • Attendance of exams is mandatory. If you must miss the midterm test due to extraordinary circumstances beyond your control (a letter from the Dean of Students will be required in this case), your 20 points of the midterm test will be automatically transferred to the final exam, i.e., your final will be graded out of 40 points. No makeup for midterm test will be allowed for any reason. If you miss the midterm without a supporting letter from the Dean of Students, there will be 5 points penalty, i.e., the maximum score you can earn in your final exam is 35/40.
  • If you must miss the final exam due to extraordinary circumstances beyond your control (a letter from the Dean of Students will be required in this case), your grade in the course will be prorated based on the components of your term work. No makeup for the final exam will be allowed for any reason.
  • The academic performance of the students in this course will be held to the standards of Union College's Honor Code.
  • Students with disabilities will be accommodated as per Union College's Policy.
  • Chang, Kang-tsung (2018). Introduction to Geographic Information Systems, 9th Edition, McGraw Hill Higher Education, New York (ISBN 1259929647).
  • Law, Michael and Collins, Amy (2018). Getting to Know ArcGIS Desktop, 5th Edition, ESRI Press, Redlands CA (ISBN 1589485106). (This book comes with 180 day license of the latest release of ArcGIS program).

ANTICIPATED OUTCOME

Gain an understanding of GIS principles.

Gain an introductory knowledge of popular GIS software tools.

Understand how to obtain spatial data from various sources.

Gain an introductory knowledge of GIS data analysis and modeling.

Use GIS tools in developing solutions to real world problems.

Practice spatial communication skills and use these skills in practical applications.

Geographically Referenced Data

Organization of This Book

Geographic Coordinate System

Commonly Used Map Projections

Projected Coordinate Systems

Working with Coordinate Systems in GIS

Representation of Simple Features

Nontopological Vector Data

Data Models for Composite Features

The Geodatabase Data Model

Advantages of the Geodatabase Data Model

Elements of the Raster Data Model

Integration of Raster and Vector Data

Conversion of Existing Data

Geometric Transformations

Root Mean Square (RMS) Error

Interpretation of RMS Errors on Digitized Maps

Resampling of Pixel Values

Spatial Data Accuracy Standards

8.6 Other Editing Operations

Attribute Data Input and Management

Manipulation of Fields and Attribute Data

Data Display and Cartography

Data Analysis Environment

Distance Measure Operations

Other Raster Data Operations

Comparison of Vector- and Raster-based Data Analysis

Terrain Mapping and Analysis

Data for Terrain Mapping and Analysis

Viewsheds and Watersheds

Parameters of Viewshed Analysis

Applications of Viewshed Analysis

Factors Influencing Watershed Analysis

Applications of Watershed Analysis

Spatial Interpolation

Elements of Spatial Interpolation

Comparison of Spatial Interpolation Methods

Geocoding and Dynamic Segmentation

Applications of Geocoding

Applications of Dynamic Segmentation

Path Analysis and Network Applications

Applications of Path Analysis

Putting Together a Network

GIS Models and Modeling

Basic Elements of GIS Modeling

Assignment (1): Introduction & Coordinate Systems

Assignment (2): Georelational & Object-Based Vector Data Models

Assignment (3): Raster Data Model & Data Input

Assignment (4): Geometric Transformation & Spatial Data Editing

Assignment (5): Attribute Data Input and Management & Data Display and Cartography

Assignment (6): Data Exploration & Vector Data Analysis

Assignment (7): Raster Data Analysis & Terrain Mapping and Analysis

Assignment (8): Viewsheds and Watersheds & Spatial Interpolation

Assignment (9): Geocoding and Dynamic Segmentation, Path Analysis and Network Applications & GIS Models and Modeling

PROJECT GIS TREAT (GIST)

Project GIST is an exciting GIS-based project that gives the students the opportunity to put into practice the knowledge gained in this course. The project entails critical thinking of a problem with spatial nature in order to identify a solution that is based on a rationale involving convincing reasoning. Students are to work in teams of two partners. Team partners will receive the same grade in the project. It is left to the students to team up with partners with common areas of interest and who share ultimate terminal goals.

Each team of two partners is given absolute freedom in selecting the project subject they like to investigate and the problem they like to address. Students in this course come from various departments. Teams may wish to address in their project a problem that is closely related to their major since GIS is a tool which can be applicable to all sorts of problems. Students may also wish to explore a new field of interest or use a theme of a subject that has intrigued them. Students must realize, however, that finding the data required to work on their selected projects could be a problem. The data students are looking for may or may not exist, or it may be available in a format that makes the accomplishment of the task too difficult or non-feasible. Data availability could be a real hindrance and the scope of the selected project should be achievable with data that is possible to obtain. Students may also wish to make their own data using the techniques learned in class (creating a database, scanning, digitizing, or reduction/expansion of existing data).

In the sixth week of the term, each team is required to submit a progress report. This should include the names of the partners, title of the project, a statement describing the subject, methodology to be used in the analysis, flow chart showing the steps to be used in implementing the solution, and anticipated final outcome. The instructor will provide feedback and approve the project subject if it involves the expected level of rigor.

In the ninth week of the term, each team is expected to submit the following:

1. A report disseminating all the information related to the project including the problem it attempted to address, data used, data source(s), analytical approach, results of analysis, and conclusions.

2. The report should contain any and all relevant information including illustrations, tables, graphs, charts, maps, and models used in the analysis.

The grading criteria will place equal weight on the following components:

1. Level of sophistication in addressing the project subject.

2. Methodology used in the analysis.

3. Accuracy and validity of analytical approach.

4. Critical thinking used in identifying a solution and reaching conclusions.

5. Project presentation as described below.

In the tenth week of the term, each team will be required to make a class presentation of their project. Teams are expected to show fully functional projects including ArcGIS demonstration. Each presentation will be followed by questions and answers period.

RECOGNITION AND OPPORTUNITIES

1. The instructor will sponsor the top three projects for presentation at Union College's Steinmetz Symposium and other local, regional, or national venues.

2. The instructor will sponsor the top-rated project for presentation at local, regional, or national conferences.

3. The instructor will nominate the top-rated project for the Ashraf M. Ghaly Geo Research Prize which is awarded annually and includes a cash prize.


Astronomy/Planetary

Asteroids – Gazing the sky and tracking asteroids with NASA’s bolide events map. (NASA’s Bolide Events)

Mapping Mars with MOLA – Start mapping a whole entire new planet using NASA’s MOLA. (USGS Planetary GIS Web Server – PIGWAD)

Mars Terrain – Going for a spin on the rugged terrain of Mars using data captured by the Mars Orbiter Laser Altimeter (MOLA) instrument on the Mars Global Surveyor (MGS). (Mars Terrain)

Mars Rover Landing – Examining how to landing the Mars Rover safely with operations criteria including latitude for solar power, soil softness, slopes using laser altimetry, dustiness, rockiness and a landing footprint.

Water Flow on Mars – Hillshading the Mars Digital Elevation Model to augment legibility and understand where rivers may have flowed and oceans flourished. (Mars Water Flow)

Satellite Orbits – Gazing the sky for satellites and even programming satellites for image acquisition. (Satellite Map)

Magnetic Fields – Investigating magnetic field lines in 3D with international geomagnetic field maps.

Astrogeology – Delivering planetary mapping to the international science community in public domain – from planetary topology to lunar geology. (Astrogeology Science Centre)

UFO Sightings – Speculating UFO sightings with proportional symbols with over 90,000 reports dating back to 1905. m

Light Pollution – Recognizing the artificial light introduced by humans in the night sky and how it interferes with the observation of stars. (NOAA’s VIIRS data) / Light Pollution Map)

Mars in Google Earth – Searching for Martian landmarks with Google Earth’s “Live from Mars” layer.

International Space Station – Tracking the real-time location of the International Space Station (ISS) in ArcGIS Online Data.

Venus – Mapping the altimetry, shaded relief and geology of Venus. (Venus Map)

Magnetic Declination – Positioning with the magnetic declination, a varying angle from a true geographic north using NOAA National Geophysical Data Center 2015 data and the Magnetic Declination QGIS Plugin.

Gravity Anomaly – Understanding our Earth’s gravity by mapping the unusual concentrations of mass in a different regions on Earth. (The Geoid)

NASA Visible Earth – Cataloging images and animations of our home planet in the electromagnetic spectrum from various sensors. (NASA Visible Earth)

Tycho – Mapping Tycho, the youngest moon crater.

Milky Way – Surveying the inner part of the Milky Way Galaxy with GLIMPSE (Galactic Legacy Infrared Midplane Extraordinaire)


2 Datasets and Software

The datasets used are described below and are publicly available online (Table 1).

2.1 Mastcam Datasets

Mastcam images and metadata are posted on the NASA PDS (Planetary Data System) Cartography and Imaging Sciences Node (Table 1) under successive volumes with the conventional name “MSLMST_00NN” (where NN currently goes from 01 to 24), and in the “DATA” folders. Mastcam data are released on a regular schedule (every 4 to 6 months, see http://pds-geosciences.wustl.edu/missions/msl/). It is also available via the MSL Analyst's Notebook (https://an.rsl.wustl.edu/msl/), which provides an interactive interface for visualizing Curiosity's traverse and for accessing data collected by the rover on each Sol and at each visited location.

Mastcam data naming conventions uniquely identify an image or metadata product. The first 4 digits correspond to the Sol during which the image was acquired, and the letters in positions 5 and 6 correspond to the camera name: “MR” for Mastcam Right or “ML” for Mastcam Left (Malin et al., 2013 , Table 3.4-1, Section 3.4).

2.1.1 Mastcam Images

The Mastcam images on the PDS are in “.IMG” format (binary image data) (Malin et al., 2013 ). In this study, we work with Mastcam RDR (Reduced Data Record) images that have been decompressed, radiometrically calibrated, color corrected or contrast stretched, and linearized: This is reflected in their naming convention, where the digits in position 27 to 30 state “DRCL” (Malin et al., 2013 ). For example, image 1429MR0070680170702598E01_DRCL.IMG is posted online at https://pds-imaging.jpl.nasa.gov/data/msl/MSLMST_0014/DATA/RDR/SURFACE/1429/.

2.1.2 Mastcam Image Metadata

For each Mastcam image, its corresponding metadata is in an associated label (a text file in “.LBL” format) (Malin et al., 2013 , Appendix A). Labels include information about images properties and about the location of the rover when the image was acquired.

2.2 Orbital Datasets and Curiosity Rover's Path at Gale Crater

The Gale crater orthophoto mosaic and DEM, which we here use as a basemap for Curiosity rover's path, are available on the Annex of the PDS Cartography & Imaging Sciences Node USGS website (Table 1). The mosaic was assembled by (Calef & Parker, 2016 ) from HiRISE and CTX data. The associated DEM (Digital Elevation Model) provides the topography of the terrains, at 1 m/pixel postings (Calef & Parker, 2016 ). It was built from HRSC (High Resolution Stereo Camera) data from the ESA Mars Express spacecraft as well as CTX and HiRISE data from MRO spacecraft (Calef & Parker, 2016 ). Both the mosaic and the DEM are raster graphics images, in GeoTIFF format.

The Curiosity rover's successive locations on each Sol are publicly available as a plain text table (in CSV format) on the PDS (Table 1), where they are expressed both in rover coordinate frame (“Site” and “Drive,” defined as successive position of the rover MSL Coordinate Systems, 2013 ) and in the corresponding latitude and longitude values. Here we use the “easting” and “northing” coordinates (in meters). These fields respectively correspond to the longitude and latitude coordinates, in the Equidistant Cylindrical meter units that match the basemap projection (Calef & Parker, 2016 ).

2.3 ArcGIS® Project and Built-In Viewshed Tool

The ESRI ArcGIS® software (version 10.5) is used here to build an interactive GIS (geographic information system) project that displays the rover path on the orbital mosaic and DEM of Gale crater.

In addition, we use the ArcGIS® built-in Viewshed tool that determines the raster surface locations visible to a set of observer features [“Using Viewshed and Observer Points for visibility analysis” https://desktop.arcgis.com/en/arcmap/10.5/tools/spatial-analyst-toolbox/using-viewshed-and-observer-points-for-visibility.htm]. The Viewshed tool uses the location of an observer on a DEM to identify raster cells that lie within (and outside) of the field of view of the observer at their precise location. Because this tool allows inspection of a limited region of the raster, we use it to identify on the Mars orbital data the terrains that are visible (1) from the position of the rover onto the Gale crater DEM at the time a given Mastcam image was acquired and (2) from the Mastcam imager point of view at that given time (section 3, Step 3). This process highlights on the orbital image the area(s) that correspond to what is observed in the Mastcam image. We term these highlighted areas “Mastcam image viewshed.”


Syntax

The feature class that identifies the observer locations.

The input can be point or polyline features.

Number of ground x,y units in one surface z unit.

The z-factor adjusts the units of measure for the z units when they are different from the x,y units of the input surface. The z-values of the input surface are multiplied by the z-factor when calculating the final output surface.

If the x,y units and z units are in the same units of measure, the z-factor is 1. This is the default.

If the x,y units and z units are in different units of measure, the z-factor must be set to the appropriate factor, or the results will be incorrect. For example, if your z units are feet and your x,y units are meters, you would use a z-factor of 0.3048 to convert your z units from feet to meters (1 foot = 0.3048 meter).

Allows correction for the earth's curvature.

  • FLAT_EARTH — No curvature correction will be applied. This is the default.
  • CURVED_EARTH — Curvature correction will be applied.

Coefficient of the refraction of visible light in air.

The output above ground level (AGL) raster.

The AGL result is a raster where each cell value is the minimum height that must be added to an otherwise nonvisible cell to make it visible by at least one observer.

Cells that were already visible will have a value of 0 in this output raster.

Return Value

The output will only record the number of times that each cell location in the input surface raster can be seen by the input observation points (or vertices for polylines). The observation frequency will be recorded in the VALUE item in the output raster's attribute table.


Contents

A viewshed analysis can be performed using one of many GIS programs, such as ArcGIS Pro, GRASS GIS (r.los, r.viewshed), QGIS (viewshed plugin), [1] LuciadLightspeed, LuciadMobile, SAGA GIS (Visibility), TNT Mips, ArcMap, Maptitude, ERDAS IMAGINE. A viewshed is created from a DEM by using an algorithm that estimates the difference of elevation from one cell (the viewpoint cell) to the next (the target cell). To determine the visibility of a target cell, each cell between the viewpoint cell and target cell is examined for line of sight. Where cells of higher value are between the viewpoint and target cells the line of sight is blocked. If the line of sight is blocked then the target cell is determined to not be part of the viewshed. If it is not blocked then it is included in the viewshed. [2]

The algorithm is also based on a given set of variables. When performing a viewshed analysis, several variables can be used to limit or adjust the calculation. For example, if the analysis is to determine the location of a radio tower, the height of the tower could be added to the elevation of that location (cell value). If no height is given, then the viewshed analysis uses the cell value of the DEM in which the tower is located.

Another way to add the height of the tower is to use an offset variable. Offset values can be added to a sending tower as well as a receiving tower. The offset value is then added to the elevation value of the cell to obtain the actual elevation of each tower.

The viewshed analysis can also have a limited viewing angle. The viewing angle, or azimuth, of the radio tower can be incorporated into the calculation by adding two values. The first value is the lowest possible azimuth angle and the second value is the highest possible azimuth angle. The program will analyze the viewshed only within these given azimuth angles. A vertical angle can be added as well. The values for vertical angle are from 90° (looking straight up) to -90° (looking straight down). This variable would need to be added in cases where the radio tower emits a very narrow vertical beam. The final variable used in the viewshed analysis is the radius value. In the case of the radio tower, if the radio signal has a limited range, perhaps 10 miles, then the radius variable can be set to limit the viewshed analysis to a 10-mile radius.

Besides tower placement a viewshed analysis can be used for other applications. For example, a viewshed analysis could estimate the impact of the addition of a large building. The viewshed analysis would show all the areas from which the building could be seen as well as any views that would be obscured from any particular location. Viewshed analyses are also used to locate fire observation stations in mountain areas (Lee and Stucky, 1998). This allows the stations to be placed so that the entire forest can be observed for possible fires.


When Storing Your Spatial Data, Which Approach Is Better, Shapefiles Or Geodatabases? There’s Only One Way To Find Out….FIGHT!!

Apologies to Harry Hill for plagiarising one of his catchphrases, but I think this is an interesting question and one that is worthy of discussion. From ArcGIS 9 onwards, there was a shift away from storing spatial data using individual shapefiles and raster grids (what I’ll call the ‘shapefile approach’) and towards storing all spatial data for a GIS project in a single Geodatabase (the ‘geodatabase approach’). Most GIS textbooks, instructional information and, indeed, training courses, for ArcGIS now seem to recommend the use of Geodatabases, but these generally seem to be aimed at large organisation with very big and complex GISs that are accessed by many different people for many different purposes. The situation in ecological research is often very different, and it is much more likely to be one person and a laptop with a relatively small and simple GIS. So when using GIS for ecological research, is there actually any advantage of using one approach over the other?

1. Geodatabases contain all the information in a single file, while using shapefiles and rasters requires that all the data layers are stored as separate files: This can be seen either as a benefit of using geodatabases or as a disadvantage. With a single file, it’s much easier to keep track of all the data layers and to back up or transfer your data between computers. It also means that its easier to ensure that working on the same files (this can be a problem with shapefiles that have a nasty habit of multiplying!). However, it also means that if something goes wrong with that single file, you are completely screwed. At least if you’re using shapefiles and your project crashes, you can easily re-build it from the individual shapefiles themselves as they are stored separately from the project file.

2. Geodatabases are specifically designed to work with ArcGIS – Part one: This is not a problem as long as you continue to have access to ArcGIS. However, what happens when the ArcGIS licence for your project runs, or you have to move institutions and no longer have a licence? If you’ve used the geodatabase approach, you may find that cannot access all your GIS data any more. If you use the shapefile approach, there are many alternative, and often free, GIS software packages out there that you can use to access, explore, plot and manipulate your data layers. Therefore, if you are unsure where your next ArcGIS licence might come from (and I am sure this is true for many ecologists), using the shapefile approach means that you will always be able to access your data no matter what. This may not always be the case if you use the geodatabase approach.

3. Geodatabases are specifically designed to work with ArcGIS – Part two: Because geodatabases are specifically designed to work with ArcGIS, you can take full advantage of all the whistles and bells of the ArcGIS software. However, it also means that you cannot easily access your data layers using different GIS software packages. While this might not always be an issue, there are often instances in ecological research where ArcGIS just can’t do what you want it to and you find that you wish to use a different software package (e.g. doing a viewshed analysis in GRASS so that you don’t have to pay for the expensive spatial analyst tools extension to the basic ArcGIS software package just to do one thing). If you use the geodatabase approach you may find that you can’t easily do this, whereas it’s much easier to seamlessly move between different software packages if you use the shapefile approach.

4. Geodatabases are specifically designed to work with ArcGIS – Part three: If you are working with people from different organisations/research groups and not everyone has an ArcGIS licence, you may find sharing your data difficult if you use the geodatabase approach. However, with the shapefile approach sharing your data layers with people using other GIS software packages is much easier.

5. Geodatabases are specifically designed to work with ArcGIS – Part four: If you learn all your GIS using ArcGIS and geodatabases, you may find that you cannot as easily transfer this knowledge to other GIS software, and especially to free, open source GIS software. This is not a problem if you can guarantee that you will always have access to ArcGIS for the rest of your research career, but if you think you might one day have to rely on using different GIS software, you may find it much easier to transfer your skills if you are at least familiar with the shapefile approach. This also means that if you start with GIS career using the shapefile approach even if you’re doing it with ArcGIS, you can then choose whether to specialise in this software and move onto geodatabases, or whether to move on to other GIS software.

6. Geodatabases are more difficult to learn to use for complete beginners: One of the main limitations for encouraging ecologists to use GIS in their research is that they get put off by over-complicated explanations of what GIS is and how it can be used. I’ve found that if I can get people playing around with real data layers as soon as possible they see how useful a tool GIS can be for their research, and they will persist with it. If they don’t see this within the first few hours of using GIS, they will often abandon it, after all there are a lot of other key research skills out there that they can spend their time learning that will also benefit their research. One of the main reason that I tend to teach people GIS using the shapefile approach, is that it gets them up and running, working with real data, as quickly as possible (often within minutes if I’m sitting with them and using their own data in a one-on-one session). If they become sufficiently interested, they can explore whether they would prefer using the geodatabase approach for their own work. If I start by teaching the geodatabase approach, I have to spend those first precious minutes explaining how geodatabases are structured, how they work, the terminology, and so on. I will then quickly see their eyes glaze over and know that they’ll be lost from GIS forever.

From the above, you will clearly see that I favour the shapefile approach, I think it is more flexible and it means that you are much less chained to using ArcGIS whether you like it or not. However, it is worth emphasising here that the bottom line is that you should use the approach that is best suited you and your own circumstances. If you like the geodatabase approach, go with it, if you like the shapefile approach, why not use it? In the end, GIS is just a tool to help you do your research. As long as you succeed in do what you need to, it doesn’t really matter which approach you take, and don’t let anyone tell you anything different.


ArcGIS Viewshed keeps crashing - Geographic Information Systems

Welcome to the last course of the specialization (unless your continuing on to the capstone project, of course!). Using the knowledge you’ve learned about ArcGIS, complete technical tasks such raster calculations and suitability analysis. In this class you will become comfortable with spatial analysis and applications within GIS during four week-long modules: Week 1: You'll learn all about remotely sensed and satellite imagery, and be introduced to the electromagnetic spectrum. At the end of this week, you'll be able to find and download satellite imagery online and use it for two common types of analysis: NDVI and trained classification. Week 2: You'll learn how to use ModelBuilder to create large processing workflows that use parameters, preconditions, variables, and a new set of tools. We'll also explore a few topics that we don't really have time to discuss in detail, but might whet your appetite for future learning in other avenues: geocoding, time-enabled data, spatial statistics, and ArcGIS Pro. Week 3: In week three, we'll make and use digital elevation models using some new, specific tools such as the cut fill tool, hillshades, viewsheds and more. We'll also go through a few common algorithms including a very important one: the suitability analysis. Week 4: We'll begin the final week by talking about a few spatial analyst tools we haven't yet touched on in the specialization: Region Group to make our own zones, Focal Statistics to smooth a hillshade, Reclassify to change values, and Point Density to create a density surface. Finally, we'll wrap up by talking about a few more things that you might want to explore more as you start working on learning about GIS topics on your own. Take Geospatial and Environmental Analysis as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first, second, and third courses in this specialization, "Fundamentals of GIS," "GIS Data Formats, Design, and Quality", and "Geospatial and Environmental Analysis," respectively, before taking this course. By completing the fourth class you will gain the skills needed to succeed in the Specialization capstone.

Получаемые навыки

Geographic Information System (GIS), Imagery Analysis, Spatial Analysis, satellite imagery, Gis Applications

Рецензии

Very good course, but certain topics evaluated in this course were not well explain, specially remote sensing images processing (downloading and managing them to GIS)

An amazing course! well organized, very informative, and rich with resources and useful materials, with an excellent discussion forum to discuss the course subjects.

In this module, we'll be looking at DEMs and workflows. In the first half, we'll make and use a handful of products derived from digital elevation models, including contour lines, hillshades, viewsheds, and the cut fill tool. In the second half of the module, we'll go through a few common algorithms, starting with one of the most important ones to know: the suitability analysis. Then, we'll learn about the hydrologic processing you did in the tutorial assignment for the previous module and go through it step by step together.

Преподаватели

Nick Santos

Текст видео

[MUSIC] Hello again and welcome back. In this lecture, I'm going to show you how to do a viewshed analysis or visibility analysis that takes into account that terrain in an area and tells you whether or not specific locations can see other locations on the landscape. We'll use two tools in ArcGIS, the Viewshed Tool and the Visibility Tool on the process, since that each provide a couple unique capabilities there. The viewshed tool is a little simpler, and the visibility tool is a little more powerful. And so it's nice to learn with the viewshed tool and also consider using the visibility tool when you have a bigger question to answer. So in Viewshed analysis, we have two broad questions we might want to ask. One is, if we have locations like these observer locations here maybe these are some sort of watch tower for fire or something like that. I'll just mark them with even though this is a school symbol, I'll mark them with this since it looks a little more like a tower and we can see them a little better. So may we have all these locations that we might want to put fire lookouts and we're trying to see how much little landscape can actually be seen from these locations. The other side of it is can these locations be seen from the rest of the landscape? So maybe if you have something that you want to hide like a cell tower, but you still want to have good coverage, you want to know which locations it's going to be seen from. Or maybe you have wind turbines which some people don't like to see, some people do. And you want to know which locations they're going to be visible from because you want to kind of keep them out of sight or something. The Viewshed Analysis can tell you based upon the locations you planned to put those turbines, which locations they're going to be visible from. So what we need for is the locations we're considering locating something or that we already have something and then a surface model like digital elevation model, and I can run the Viewshed tool. So when I open it up like I said you need input raster which is a digital elevation model and then we need the point old line server feature so in this case we have point locations. And we'll call this, we'll pretend that these are fire lookout locations. So we'll say fire lookout. Viewsheds, and we'll also output above ground level raster. And what this does for us is it tells us for locations that can't be seen how much higher update would need to be before they could be seen by at least one point in our observer locations. So we'll write this out to a geodatabase as well and we'll call this, I'm going to call it AGL for fire lookouts and where AGL stands for above ground level. And I'm not going to use earth curvatures correction in this case this is a pretty small area and I think it's dominated much more by terrain and we have a good distribution of points which should mean that mostly the earth curvature won't factor in. You could if you wanted a much more accurate choice in this case. But we'll click okay, and while that's running, let's take a look at invisibility tool because it's very similar but has far more options. Specifically related to parameters we could set for the observer locations. So once we see what's possible that the view shed will fill this in as well. In the meantime, I'm going to set up the visibility by selecting the Navarro River digital elevation model and then our same server locations and we'll call this In this case we're going to do it as if these are wind turbines. So I'll call it turbine visibility. So visibility tool provides a good reverse because if we consider that wind turbines have a height, we didn't select their height in the view shed tool. And so what we might want to do is say that these are actually so high off the ground so that they could be seen maybe from further away or from lower down. And that's where our observer parameters come in here. We can select an observer off set which allows us to say, well maybe the wind turbines stand 100 meters tall. And so it will calculate a different view shed for us based upon these being 100 meters tall and then we can access what can be seen from the top of them and well as where the tops of them can be seen from. And again, I'll leave the rest of this as standard but you can definitely look through it for additional options that can help your view shed analysis. And I'll have it run when the Viewshed tool is done. Okay, so our Viewshed finished and all the areas in light green here, let's change these colors a bit. I'm going to make it, Kind of a blueish, deep blue. All the areas in blue can be seen from the lookouts, and all the areas we make it kind of. Yellow here. All the areas in yellow can't be seen, and now let's change these just to a black, can't be seen. So all areas in blue can be seen from the lookout locations, and all the areas in yellow cannot be seen. Now if we were doing this as like a site suitability assessment, maybe we would want to start moving around these points to see if we could get better locations on the ground. For a fire lookout you maybe you just want to find better coverage where you get points in many locations because you would be able to see smoke rising up out of these canyons, regardless. Ans maybe youɽ want to see spots where you can look up and down canyons more. Now, if we concerned about Being able to see each location a little more, the above ground level raster shows us almost like a reverse digital elevation model. It shows us how much taller these things need to be in order to be seen from at least one lookout. So these spots deep in the canyons in here, if I turn it off we could see that this is a canyon here. Those deep canyons Are much harder to see from either of these lookouts here. So they need to be a lot taller in order to be captured and seen by at least one lookout. So what you might want to do is maybe locate another point near them, or in a spot that has better visibility of that location. Now that other visibility analysis just finish where we did our turbines. And notice that by making them 100 meters taller above the landscape, they're seen by far more locations. And part because these spots are right at the tops of hills if you noticed, because they were originally meant to be fire lookouts so they're supposed to sit on top of hills. One second, let me just copy the symbology from the fire lookouts over so we can look at the same thing. So if we compare these two, we can see that if yellow is not visible and blue is visible, by raising them up 100 meters above the surface as we can do with the visibility tool and not in the Viewshed tool, they're seen by far more locations. So if you are talking about items that are high above the landscape then you're really kind of want to build the use the visibility tools that you can assess their actual height. Okay, that's it for now. This is mostly an introduction to Viewshed assessment. There are a lot of different ways to do it and as there are even has some online tools where you could do a Viewshed kind of across from a location on a surface by drawing a line from the observer location out across the surface and it will tell you kind of as a profile which locations are and aren't visible in that line of sight. So it's not just one way of doing this things but there is a concept behind it. And I want to make sure that you know that with the digital elevation model and some point of interest you can do something like Viewshed assessment. Again, the key take way here is that it's really easy to do a Viewshed with the Viewshed tool you don't need a whole lot of information, you just need the digital elevation model and the locations of interest. And then the visibility tool and the Viewshed tool too that we didn't use in this lecture. Each have quite a few more options that let you get a more accurate assessment based on whatever you are working with. Whether it's something that's high above the landscape or whether you need to make surface corrections. Okay, see you next time.


  • The Geoprocessing service example: Watershed tutorial preprocesses hydrologic data by creating a flow accumulation and direction raster.
  • You can precompute distances from known locations using the Near or Generate Near Table tools. For example, suppose your service allows clients to select vacant parcels that are a user-defined distance from the Los Angeles River. You could use the Select Layer By Location tool to perform this selection, but it would be much faster to precompute the distance of every parcel from the Los Angeles River (using the Near tool) and store the computed distance as an attribute of the parcels. You would index this attribute using the Add Attribute Index tool. Now when the client issues a query, your task can perform a simple and fast attribute selection on the distance attribute rather than a less efficient spatial query.

If your task is selecting data using attribute queries, create an attribute index for each attribute used in queries. You can use the Add Attribute Index tool. You only need to create the index once, and you do so outside of your model or script.


Weekly Discussion: GIS & Computer Hardware Specs

It might be helpful to provide context as well, like if you have tons of processing power, what kind of work are you running? QGIS/ESRI/other? Do you have multiple instances of software running concurrently? Any GIS best practices that go along with certain specs are helpful as well.

Just as a reminder, the point of these weekly discussion posts is to potentially use this information as a resource for other GIS users that visit r/gis . The plan is to assemble these discussion posts on the wiki, so please contribute if you can! Thanks :)

4 GB RAM (not enough to run a decent viewshed in QGIS but I'll get an upgrade on Monday, yay!)

wacom bamboo (an old one, 2010 or so)

Runs QGIS 2.16, ArcGIS 10.4.1

However, I do most of my work on a remote machine in another country, which sits next to the server on which our data is stored:

4 x Intel Xeon E5-2609 2.4 GHz

ArcGIS 10.4, but no QGIS because I have to jump through at least 37 hoops to get it installed on there.

Edit: should mention that I'm not the only one working on the remote computer, up to 8 people can use it at the same time.

I never thought about this. Can you say, get subscriptions to remote machines running the latest version of ESRI software? Lets say I'm self-employed and I get a contract to do some work, but I don't want to pay to keep an ArcGIS license because I only do this on the side. I basically call up my buddy in Mumbai, and he lets me use his super computer with ArcGIS, AutoCad, Revit, Adobe, whatever, remotely for $30 a month.

Intel Xeon E5-1660 6 Core 3.3GHz

nVidia Quardo 2000 running 2 monitors

nVidia Quadro FX 580 running the third monitor

Built for ArcGIS and Civil 3D. Though lately I've been doing a lot of work in Excel -_-

Processor: Intel Core i5-6600K (3.5 GHz)

System Type (64 bit, 32 bit): 64 Bit

Number of monitors: 3 (2 Acer 24 inch LED monitors and a Acer Predator Gaming Monitor [when I game])

Mouse: some old mouse I've had for several years.

I'm currently an intern at a county water district and finishing my undergrad. I built this personal computer primarily for gaming. It's overkill for GIS related work.

Has anyone used a Wacom board for GIS? I've been creating a map index for the past two days on a conventional PC and can't help thinking a Wacom board/ surface board would ease my hand cramping. IDK, thoughts?

Just started a new job and got lucky enough to have this baby:

maybe you can use this thread to leverage more RAM, lol

Using my work's standard issue:

2 additional monitors (only GIS kids get 2 additional)

We're running ArcGIS 10.2 looking into changing things over to 10.4 or potentially 10.5 (when the time comes). Hardly any 3D work to speak of. We've done the odd task that requires 3d analyst. Any issues are usually software glitches or ridiculously large datasets (Anything 8GB and larger.)

Three set ups, all of which I have connected to my Logitech MX Master mouse.

ArcGIS 10.3, QGIS, Excel/Access, Python

128GB SSD Boot + 2x 3TB HDD

QGIS, Pix4D, Agisoft Photoscan, Meshmixer, Videogames

QGIS, Excel/Access, Light gaming, Chrome, glorified SD Reader, Teamviewer to above

Processor - AMD Phenom II x6 1090T @ 3.2ghz

System Type (64 bit, 32 bit): 64 Bit

Video Card - AMD Radeon HD 6800 Series (needs to be upgraded)

Built this computer about 5 years ago before I even knew about GIS. It's still a beast. I've been very happy with it but the videocard does need to be upgraded. This is not my work computer, it's my personal. I toy around with arcmap and some scripting on it, nothing too intensive. It handles arc very well.

-WIN7 -i7-4790 -RAM DDR4 32GB -2 Dell monitors -Logitech G502 mouse

Use this computer to Run FLDPLN and ArcGIS 10.3

I was really shocked when I learnt that arc didn'r run on might macs. I tried the Parallels virtual machine and it was terrible, I mean even without arc, parallels was total crap so I tried bootcamp but it wasn't that good either. Windows looked washed out on the mac and while I was capable of doing most GIS work on it, I didn't really see any performance improvements due to the better specs. This was back in 2014. I am still waiting for the day when esri starts respecting itself and its clients and ship something that's better than crappy, though the empirical evidence proves that day will never come.

Xeon E5-2630v2 6 core @ 2.60ghz

32GB RAM (Going to upgrade to 64 in a couple of weeks)

Best mouse ever Logitech G500 <3

Using VirtuaWin to get four virtual desktops to differentiate between all the parallel projects I've got going. I'm mainly using Qgis,Arcmap, Globalmapper, FME depending on the task at hand.

Best GIS-practice, USE virtualWIN and remember FME can do anything your heart desires!

2 1920x1080 27inch IPS monitors

7 button logitec mouse and a Wacom Tablet

QGIS, mapbox studio, photoshop, illustrator.

I do lots of spatial analysis and design work with large datasets.

Wacom masterrace! What kind do you have? I've got an old Bamboo, but I've also got an Intuos 4 Large at home.

Linux 2.6 GHz Intel Core i5 8 GB RAM, 16 GB SWAP 64 bit 2 Monitors (14" laptop and 23" widescreen) 5-button Logitech wireless mouse

I mostly use it with QGIS, GDAL and PyQGIS, but I also do some map design with InkScape. I have my /tmp drive in a RAMFS to reduce SSD write frequency, but you should only really do that if you have a decent amount of RAM to spare.

MacBook Pro (Retina, 15-inch, Mid 2015)

AMD Radeon R9 M370X 2048 MB

Apple Thunderbolt Display - 27-inch (2560 x 1440)

I do ArcGIS work through VMWare Fusion

ThinkPad running Win7SP1 x64

Microsoft Ergonomic 4000 keyboard

On this computer, I run simple (e.g. spatial statistics) GP tools on >30000 points distributed over time and space with ArcGIS Pro. I also do a bit of OpenStreetMap work with JOSM. This computer is my personal machine.

My work computer is a Dell Vostro (Intel Core i7, Win10 x64, 8 GB RAM, 2 monitors, same mouse & keyboard) and my GIS work primarily consists of working with simple feature classes and building web maps.

For licensing reasons, I do all my service publishing and Python automation on a VM running Windows Server 2012 via Remote Desktop.

I'm a researcher at a university. My main machine is a compute server accessed through Remote Desktop. I do a lot of lidar analysis and massive raster modeling, so it's still pretty easy to crash if I'm not careful! I mainly work in R, which is a memory hog (and CPU hog when you run enough parallel processes). I also run arcpy/ESRI products, which aren't really able to take advantage of the resources. It runs just as fast on a decent desktop.

Main machine: Rack mounted server OS: Windows Server 2012 R2 64-bit RAM: 48GB DDR3 CPU: Dual Intel Xeon 16-core 2.60GHz (32 total cores) Logitech M570 mouse

Tomorrow I'll look up the specs of a few of the clusters and UNIX servers I use less often, some of those are crazy (a single machine has 750+GB of RAM, and the biggest cluster has 4.5TB of RAM).


Watch the video: ArcGIS desktop has encountered a serious application error and is unable to continue. ArcMap