| dc.contributor.author | Beshah, Dawit Mekonnen | |
| dc.description.abstract | Implications of artificial intelligence around the intersection of agricultural technology and satellite remote sensing are only beginning to emerge. One of the application areas in the agriculture sector that can leverage machine learning most immediately is land use classification of remotely sensed data. This research shows that window-based aggregates can help accomplish this task by creating extra layers of information in addition to the original satellite image. Sliding window-based aggregation allows creating these processed data layers efficiently. The results show that adding the processed layers to predictive machine learning models can boost classification accuracy. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.title | Evaluating Sliding Window Multi-Scalar Analysis Importance on Land Use Classification from Satellite Remote Sensing Data | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2019-08-15T16:29:06Z | |
| dc.date.available | 2019-08-15T16:29:06Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/30216 | |
| dc.subject.lcsh | Land use -- Classification. | |
| dc.subject.lcsh | Land use -- Remote sensing -- Data processing. | |
| dc.subject.lcsh | Geospatial data. | |
| dc.subject.lcsh | Spatial data mining. | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Denton, Anne | |
