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Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists "salt-and-pepper phenomenon" in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification. [ABSTRACT FROM AUTHOR]

Many languages in Africa are written using Latin-based scripts, but often with extra diacritics (e.g. dots below in Igbo: $${\d i}, {\d o}, {\d u}$$) or modifications to the letters themselves (e.g. open vowels 'e' and 'o' in Lingala: ɛ, ɔ). While it is possible to render these characters accurately in Unicode, oftentimes keyboard input methods are not easily accessible or are cumbersome to use, and so the vast majority of electronic texts in many African languages are written in plain ASCII. We call the process of converting an ASCII text to its proper Unicode form unicodification. This paper describes an open-source package which performs automatic unicodification, implementing a variant of an algorithm described in previous work of De Pauw, Wagacha, and de Schryver. We have trained models for more than 100 languages using web data, and have evaluated each language using a range of feature sets. [ABSTRACT FROM AUTHOR]