Showing results for: [ machine learning ]
High resolution (30 m) land cover and cropping maps in GeoTIFF format for two main rice types in northern Bangladesh, dry season Boro rice (January to May) and wet season Aman rice (October to January... more) for the cropping seasons of 1989–1990 to 2015–2016. Other land cover types include other vegetated type, water, water non-permanent, and bare. The values in the Boro season are as follows: 10 represents Boro, 11 and 13 represent other vegetated areas, 14 represents water, 15 represents water non-permanent and 16 represents bare. The values in the Aman season are as follows: 20 represents Aman, 23 represents other vegetated areas, 24 represents water, 25 represents water non-permanent and 26 represents bare. Value 0 is a null value in both rice season maps. Associated GeoTIFF maps show the number of months missing in each pixel per mapping season per cropping year (using the unfilled monthly composite images) as a guide for quality. less
SDIP Phase 2 - Remote sensing crop systems analysis - Published 30 Jun 2020
Based on a general definition of a cluster and the quality of a clustering result, this code presents a new method for evaluating existing clustering algorithms, or undertaking clustering, capable of ... morepredicting the number and type of clusters and outliers present in a data set, regardless of the complexity of the distribution of points. This algorithm, referred to as iterative label spreading (ILS), can recognize the characteristics expected of a successful clustering result before any clustering algorithm has been applied, providing a type of hyper-parameter optimization for clustering. In this notebook the algorithm, is assessed using large benchmark two-dimensional synthetic data sets, with tutorial examples. less
Machine Learning Group Operating Cost - Applied Machine Learning - Published 17 Sep 2019
This collection comprises the two synthetic datasets for the assessment of the reliability of predictive process monitoring techniques used in Klinkmüller, C., van Beest, N., Weber, I.: Towards Relia... moreble Predictive Process Monitoring. CAiSE Forum, 2018.
The two datasets are provided as XES-files. For more information on the Extensible Event Stream (XES) standard see http://www.xes-standard.org.
The classes for each traces are captured via the "concept:name" sub-element. Additionally, for each trace the "classifiableFrom" sub-element records the minimum number of events that must be observed for the trace to be classifiable. More information regarding the notion of classifiability is provided in the paper.less
Legacy data - - Published 13 Apr 2018
Example code for the protein droplet processing algorithms
Legacy data - - Published 01 Mar 2018
Classification pipeline for images produced at the Collaborative Crystallisation Centre