GIS 5027 - Land Use/Land Cover Classification


The second week of Photo Interpretation and Remote Sensing was focused on Land Use/Land Cover Classification and Accuracy Assessments. Students were tasked with using the USGS Land Use/Land Cover classification system to classify a provided image of Pascagoula, Mississippi at level I and II. Once the classification was complete we then had to determine the accuracy by 'ground truthing'. As this is an online course we used Google Maps/Earth to verify random points and then calculated the accuracy. 

I ended up with a fairly low accuracy. This was mostly due to errors in my residential class. I had a few buildings that were on the cusp of a residential and commercial area but ended up in the wrong class. I also had an instance of a church being located in the middle of a residential area. My highest accuracy was in the water class, 100%. 

Comments

Post a Comment

Popular posts from this blog

GIS 5935 - Data Quality Assessment

Image
Like the second assignment, this module continues to focus on data quality. More specifically, this week focused on data quality of road networks. As such, we were tasked with performing a comparative assessment to determine the completeness of a street centerline shapefile and a Topologically Integrated Geographic Encoding and Referencing (TIGER) road shapefile for Jackson County, Oregon.  To perform this assessment we utilized a non standard method, based on that used by M. Haklay in the "Comparative Study of OpenStreetMap and Ordinance Survey datasets" in 2010. This included using a grid, which encompasses the study area, to clip the road network into smaller sections. These smaller sections give more detailed insight into areas which may be considered complete or incomplete. Once the roads were clipped to each grid cell/section the lengths were calculated and then compared.  My analysis determined that 134 of the grid cells/sections contained parts of the street centerlin
Read more

GIS 5935 - Surfaces - TINs and DEMs

Image
Having moved on from the modules regarding data quality, this week we were introduced to surfaces and surface models, more specifically Triangulated Irregular Networks (TINs) and Demographic Elevation Models (DEMs). TINs are based on vector data and are made up of a series of linked triangles that vary in shape and size, but display a 3D representation of a surface. DEMs are based on raster data, more specifically the spot heights associated with each grid part of a raster.  For this lab we were tasked with creating a TIN and DEM, as well as displaying contours based on both of them. We were then tasked with comparing the contours produced by both. Below I have included images of my results.  (a) Contour lines based on the TIN (b) Contour lines based on the DEM The contours produced as a result of the TIN appear to be more jagged, while the contours produced from the DEM have a smoother appearance. There is also a noticeable difference between the derived contours in areas that hav
Read more

GIS 5100 - Who is Gabrielle Hudson?

Image
Hi! My name is Gabrielle. I am currently a part time student in the MSc. Geographic Information Science Administration program at the University of West Florida (UWF), but I'm also a full time employee at the Bahamas National Geographic Information Systems (BNGIS) Centre, in The Bahamas. My official position is GIS Analyst and GI Education Coordinator, however due to the small staff compliment we all wear many hats. Some of my responsibilities include maritime delimitation, disaster preparedness and response, training, mentoring, data collection and data management. While I enjoy most aspects of GIS, my favorite part is producing a meaningful and esthetically pleasing map. I decided to pursue a masters level degree with the hope of increasing my knowledge in GIS, which will better prepare me for future opportunities at my current place of employment, or where ever the opportunity may present itself. The degree option at UWF seemed to perfectly match my goal of future advancement as
Read more