Introducing co-clustering for hyperspectral image analysis

This work introduces the use of co-clustering for hyperspectral image analysis. Co-clustering is able to simultaneously group samples (rows) and spectral bands (columns). This results in blocks, which do not only share spectral information (classical one way clustering) but also share sample information. Here, we propose using a co-clustering algorithm based on Information Theory - the optimal co-clustering is obtaining minimizing the loss of information between the original and the co-clustered images. A hyperspectral image (160000 samples and 40 bands) is used to illustrate this study. This image was clustered into 150 groups (50 groups of samples and 3 spectral groups). After that, blocks of the spectral groups was independently classified to assess the effectiveness of the co-clustering approach for hyperspectral band selection applications. Furthermore, the results were also compared with state-of-art methods based on morphological profiles, and the covariance matrix of the original hyperspectral image. Good results were achieved, showing the effectiveness of the Co-clustering approach for hyperspectral images in spatial-spectral classification and band selection applications

Spatial+:A new cross-validation method to evaluate geospatial machine learning models

Random cross-validation (CV) is often used to evaluate geospatial machine learning models, particularly when a limited amount of sample data are available, and collecting an extra test set is unfeasible. However, the prediction locations can be substantially different from the available sample, leading to over-optimistic evaluation results. This has fostered the development of spatial CV methods. Yet these methods only focus on spatial autocorrelation and cannot sufficiently guarantee that the validation subset is a good proxy of the test set with significant differences. In this paper, we propose the spatial+ cross-validation (SP-CV) method. This method, which considers both the geographic and feature spaces, is composed of two stages. The first stage addresses spatial autocorrelation issues by using agglomerative hierarchical clustering to divide the available sample into blocks. The second stage deals with multiple sources of differences. It uses cluster ensembles to split the blocks into training and validation folds based on the locations of the sample data and the values of the covariates and target variable. The proposed method is compared against random and block CV methods in a series of experiments with Amazon basin above ground biomass and California houseprice datasets. Our results show that SP-CV provided the smallest error differences with respect to the reference error. This means that SP-CV produced more representative splits and led to more reliable model evaluations. It suggests that a reliable model evaluation requires to consider both the geographic and the feature spaces in a comprehensive manner.</p

Integrated environmental modeling:an SDI - based framework for integrated assessment of agricultural information

Urban villages are widespread in many Chinese cities, providing affordable and accessible hous-ing for rural migrants. These urban villages are developed by the indigenous village population base on a self-help approach and in an unauthorized style. Consequently, urban villages are characterized by rapid physical development. This paper uses GIS applications and Municipal building surveys as instruments to examine the development patterns of urban villages in Shenzhen, one of the most dy-namic cities in China. Analyses reveal significant variation in development patterns and trends across urban villages, which can provide informative support for policy making associated with urban vil-lage (re)development

Open Educational Resources:Basic concepts, challenges, and business models

Besides research, education is the raison d’être of each university. Education can help close equity gaps and maintain social cohesion between and within countries. In this context, the digitisation era offers new opportunities, for example, in the form of distance and online learning. However, innovations can also come with challenges, such as employed and unemployed people requiring to adapt to a progressing working environment at ever shorter intervals (life-long learning). Consequently, it is increasingly important to gain free access to up-to-date educational materials about a wide range of subjects and at multiple academic levels. In this document, we introduce the concept of Open Educational Resources (OER). We start with establishing a definition of OER, what is needed to call educational materials OER, and the differences in comparison to related concepts, such as Massive open online courses. We then address the question of who can benefit from OER. It reports on the incentives to publish OER taking into account the perspectives of the involved stakeholders, i.e., the general public, universities and lecturers, and students. Afterwards, we pay attention to the challenges that come with OER. Subsequently, we provide a list of potential business models around OER, their underlying concepts, benefits, limitations, and projects making use of them. We also consider the paradox that OER are not intended to generate revenue but that ignoring income can make OER unsustainable. The document concludes by outlining possible steps to realize OER (e.g., organizing a round table to initiate a discussion about how to realise OER at the faculty level)