Department of Geography, Trinity College Dublin – Post-doctoral position available

Post Specification (033952)

Post Title: Research Fellow (Postdoctoral Researcher) in Geography
Post Status: Fixed-term Contract 16 months Full-time
Research Group / Department / School: Department of Geography, Trinity College Dublin, the University of Dublin
Location: Department of Geography,
Trinity College Dublin, the University of Dublin
College Green, Dublin 2, Ireland
Reports to: Dr. Cian O’Callaghan
Salary: Appointment will be made on the Research Fellow scale at a point in line with Government Pay Policy (35,488 per annum), appointment will be made no higher than Level 2 Point 1
Hours of Work: Hours of work for academic staff are those as prescribed under Public Service Agreements. For further information please follow the link below: service-agreement.pdf

Closing Date: 12 Noon (Irish Standard Time), 19 July 2019


UCD School of Geography Post-doctoral position available

UCD Post-doctoral Research Fellow Level 1, UCD School of Geography (Temporary 18 months post commencing 1 Sept. 2019)

A New Space Strategy for Ireland

The Government as launched an ambitious new Space Industry policy- National Space Strategy for Enterprise 2019-2025.

The details are here but in short its about supporting new enterprise in the Space sector, making sure Ireland leverages the opportunities in H2020 and ESA, raises awareness about Irelands role in the space industry and most importantly for us:

“Develop a sustainable Earth Observation services sector based on advanced data analytics capability”

The document, whilst having a similar set of goals as some of the old EI documents on the space sector, has identified some concrete actions and has some useful facts and figures but unfortunately the EO parts are weaker. They identify 6 goals (of which the EO one above is number 6) and give KPI for each. For the success of the EO objective:

“Number of users per year of the national space data archive”

Now there are a number of archives of satellite data in Ireland for Ireland, but most are relatively new, holding sentinel data and none are complete (for example we hold all the LANDSAT data for Ireland going back to early 80’s, but very little sentinel, no spot or AVHRR, some MODIS) and certainly none are the national space data archive. So the document states that :

“…Enterprise Ireland, working with Government departments and agencies, industry and Research Preforming Organisations will establish a Space Data Hub, allowing users to access and process data from European and other 3rd party space missions. This hub will form the core around which a vibrant internationally traded services sector, based on converting Earth Observation data into commercially valuable information, will be built.”

Before the Copernicus program began supplying data there were lots of discussions about Ireland establishing a national hub with Copernicus that went nowhere due to cost issues (and also to be fair some of the ESA structures then in place were not easy to navigate). It was felt that Ireland needed a hub as there were particular issues on processing data for Irish use (around atmospheric corrections and, cloud cover etc) that were often overlooked in products derived for a central European hub. It was also clear that if Ireland were to really maximize the usefulness of a EO hub both locally and as leverage for joining H202/ESA projects it would need to put as much effort into creating a ground truth component to the hub as it did in the EO component. This is even more true today as the biggest obstacle now to developing EO services, especially within machine learning systems, is consistent, high quality reliable ground truth. With a ground truth hub (land use data, crop growth data, grass cover etc.- a lot of this data is already collected, its just not centralized or optimized for EO work) we could position Ireland front an center in EU EO research.



Vacancy: Research Fellow (Postdoctoral Researcher) in Geography

Research Fellow (Postdoctoral Researcher) in Geography position available, Fixed-term Contract 16 months Full-time, Department of Geography, Trinity College Dublin, the University of Dublin

Closing Date: 12 Noon (Irish Standard Time), 19 July 2019

FREE TRAINING:Extracting Information from LiDAR Data Online

Extracting Information from LiDAR Data Online


This four part training will be held for two hours each Tuesday of the month of September:

September 3, 10, 17 and 24, 2019

Class Hours: 9:00 AM – 11:00 AM MST

Rules of thumb for spatial data

While browsing the internet in an endless search for a solution to my problem…a vector data-based rule-based expert knowledge classification/decision tree (manual) plugin for GIS (or software) I came across VectorMCDAQGIS plugin, k2-tree, FRAGSTATS (see paper) but also these interesting rules of thumb, among which, the last two (particularly the last one, Murphy’s law) in the list Rules of thumb in remote sensing attracted my attention:

On this page will be placed some of the rules of thumb that have been identified by practitioners in the fields of Cartography, Remote Sensing, GPS, GIS and Spatial analysis

Rules of thumb in cartography

  • # of shades of gray distinguishable (16 max).
  • # of legend categories consistently recognized (5, 7 max).
  • Symbol sizes should not vary when mapping qualitative data.
  • Raw totals should not be mapped using a choropleth map.

Rules of thumb in remote sensing

  • The # of contiguous pixels required for object identification — 10 to 16 (source TBD).
  • McCoy (see below) recommends that, considering all of the potential variables that can affect the results, at a minimum sample units should be no smaller than a 3 x 3 cluster of pixels for training sites or accuracy assessment sites.
  • What should the size of the sample site be? A useful formula for determining the area of a sample site is: A = P(1 + 2L), where A is the minimum sample site dimension, P is the image pixel dimension, and L is the estimated lcational accuracy in number of pixels. For example, say your GPS has a locational accuracy of 15m, and you are working with TM data (30m pixels). Therefore L is equal to 0.5 (15m GPS – 30m pixel -> our locational accuracy is half a pixel). A is therefore equal to 60m X 60m. This should be considered a theoretical minimum value, since it assumes that the georegistration of the TM image is perfect. Larger sample sites should typically be used in order to allow for both greater GPS uncertainty, image georegistration uncertainties, and heterogeneity on the ground. (Source: R. M. McCoy. 2005. Field methods in remote sensing. New York: The Guilford Press., p. 23)
  • M. Mather (1987), in his “Computer Processing of Remotely-Sensed Images – An Introduction“, states that the minimum number of pixels in a training sample must be 30*p per class, where p = number of features. [Please refer to the third paragraph of section 8.4.1 on Page 290 of the above mentioned book] This may not take into account the effects of spatial autocorrelation, however.
  • The number of pixels in each training set (i.e., all of the training sites for a single land cover class) should not be less than ten times the number of bands. Thus, when you are using six bands in a classification, you should aim to have no less than 60+ pixels per training set. (Source: Dr. Michael Govorov, Malaspina University College, with attribution to IDRISI).
  • For a given level of technology, there is a fixed level of ‘total resolution’ that can be obtained:
    image detail is a function of {spatial resolution, spectral resolution, radiometric resolution}. Typically, as one increases the other decreases (thus, we often find that spatial and spectral resolutions are inversely related) (e.g., SPOT Panchromatic with its spatial resolution of 10 m; SPOT Multispectral has a spatial resolution of 20 m).
  • Ross Nelson, from the NASA Goddard Space Flight Center, has developed a rule of thumb based on careful examination of the accuracy of several studies published in the remote sensing literature. Of course exceptions can be found, but these are very useful guidelines. The underlying concept is that the more precise the class definitions are the lower the accuracy will be for the individual classes. Classification Accuracy:
    • Forest/non-forest, water/no water, soil/vegetated: accuracies in the high 90%’s
    • Conifer/hardwood: 80-90%
    • Genus: 60-70%
    • Species: 40-60%

    Note: If including a Digital Elevation Model (DEM) in the classification, add 10% (Source: Biodiversity Informatics Facility of the American Museum of Natural History’s Center for Biodiversity and Conservation).

  • Ortho-corrected images can have higher absolute accuracy, but when relative accuracy is needed it may be better to use only systematically corrected images rather than mix systematically-corrected images with ortho-corrected imagery (Source: Biodiversity Informatics Facility).
  • 50% or more of the information for a given pixel contains recorded energy from the surface area surrounding that individual pixel (Source: Biodiversity Informatics Facility).
  • Clouds will always obscure that part of the imagery that is most important to your study.

New Earth Observation Post Doc in Teagasc

We are looking for an EO Post Doc as part of the SFI funded VistaMilk center. Its a 2 year position based in Teagasc, Ashtown, Dublin (in collaboration with Insight UCD).

The project is based around habitat mapping; combining random forest classification of sentinel imagery for habitats but using terrestrial photography (say a smart phone image) as a ground truth, facilitated by a priori classification of the photography through machine learning.

This is a very innovative Post Doc with wide ranging impacts beyond producing a habitat for parts of Ireland – the emphasis is on stitching together satellite data and terrestrial photography into providing a high quality evidence base for the state of farm habitat in Ireland. Details here, closing date in June 24th.