Lectures

Lecturers: Petri Rönnholm (responsible teacher), Jussi Nikander and Sami El-Mahgary

Lectures are recorded and published after the physical lecture. Physically lectures are in the hall K326 on Tuesdays and in the hall K202 on Thursdays. Notice that you need to have an access token activated to access the hall K326 (https://www.aalto.fi/en/services/how-to-get-an-access-token-and-access-rights).

L1. Tue 6.9., 10:15-12, K326, Introduction (Petri)

L2. Thu 8.9., 10:15-12, K202 U135a U7 (hall changed). Convolution and interpolation with regular data (Petri)

L3. Tue 13.9., 10:15-12, K326, Correlation and spatial autocorrelation (Petri)

L4. Thu 15.9., 10:15-12, K202 U135a U7 (hall changed). Statistics, spatial statistics and least-squares method (Petri)

L5. Tue 20.9., 10:15-12, K326, Clustering and classification (Petri)

L6. Thu 22.9., 10:15-12, K202 U135a U7 (hall changed). Spatial data structures (Jussi)

L7. Tue 27.9., 10:15-12, K326, Computational geometry (Jussi)

L8. Thu 29.9., 10:15-12, K202 M1 (Otakaari 1). Spatial decision making tools (Petri)

L9. Tue 4.10., 10:15-12, K326, Spatial quality and uncertainty (Petri)

L10. Thu 6.10., 10:15-12, K202. M1 (Otakaari 1) (Lecture is cancelled, materials will be available) Storing and handling spatial data (Sami)

Examination: Thu 20.10., 9-12. This “lecture examination” will be a remote examination in MyCourses (you can utilize all lecture materials during the examination). Other possibilities to pass the examination later (13.12.2022 and 2.2.2023) are traditional physical “evening examinations” (in Otakaari 1 at 16.30–19.30, the hall is announced in the lobby on the examination day) with a pen and paper, in which no materials can be accesses.

Assignments:

All assignments have an email support. Do not hesitate to ask for help if you face problems. To get the final grade all assignments need to be accepted.

1. Creation of orthophotos with Matlab (programming), DL 21.9. (Relates to lectures 1 and 2. Average workload estimate 11 h)

2. Spatial statistic in R (scripting), DL 28.9. (Relates to lectures 3 and 4. Average workload estimate 6 h)

3. Basics of geoinformatics with a pen and paper, DL 5.10. (Relates to lectures 1, 3, 6, and 7. Average workload estimate 7 h)

4. Implementation of k-nn and kmeans classification algorithms (in R, programming), DL 14.10.  (Relates to lecture 5. Average workload estimate 20 h, allocate enough time for this assignment)

It is okay to make programming assignments together with other students, but ensure that you learn Matlab and R programming since you will need these skills also in other courses in the Master’s programme in Geoinformatics.

Two bonus points to the exam is given for those who submit all (acceptable) reports in time (only if the examination is passed: 15/30 points).



Last modified: Tuesday, 4 October 2022, 12:20 PM