Land cover classification using super-vised and unsupervised learning techniques

Nijhawan, Rahul; Srivastava, Ishita; Shukla, Pushkar

doi:10.1109/iccids.2017.8272630

Cited by 18 publications

(9 citation statements)

References 4 publications

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“…Predictive models typically include a machine learning algorithm that learns certain properties from a training dataset. The learning process can be applied using supervised learning [ 41 , 42 ], unsupervised learning [ 42 ], semi-supervised learning [ 43 ], active learning. In the purposed method, supervised learning was employed by presenting a set of solved (labeled) examples to the model for training.…”

Section: Methodsmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

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Section: Methodsmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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“…The ISODATA method overcomes possible disadvantages of the kmeans method but requires good knowledge of the dataset which is clustered. [43] carried out a study comparing clustering methods and showed the best performance of the K-Means method.…”

Section: Used Tools and Algorithmsmentioning

confidence: 99%

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Snevajs¹,

Onckelet²,

Kvapil³

et al. 2022

Preprint

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Satellite Crop Detection technologies are focused on detection of different types of crops on the field in the early stage before harvesting. Crop detection is usually done on a time series of satellite data by classification of the desired fields. Currently, data obtained from Remote Sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops, also modern technologies using AI methods are desired in the postprocessing part. In this challenge Sentinel-1 and Sentinel-2 time series data were used due to their periodic availability. Our focus was to develop methodology for classification of time series of Sentinel 2 and Sentinel 1 data and compare how accuracy of classification can be increased, but also how to guarantee availability of data. We analyse phenology of single crops and on the basis of this analysis we started to provide crop classification. Original crop classifications were made from Enhanced Vegetation Index (EVI) layers made from Sentinel-2 time-series data and then we added also . To increase accuracy we also integrate into the process parcel borders and provide classification of fields..

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“…In contrast, the type of unsupervised learning was initiated through the association task [68] with the purpose of discovering frequent actions that were present in the bank of analogies used by professors in CS1. There were two methods used for this task: Apriori (to search for groups of frequent items) and Predictive Apriori (to extract the best rules with support and trust parameters).…”

Section: Mining Activitymentioning

confidence: 99%

Discovery Model Based on Analogies for Teaching Computer Programming

2021

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Teaching the fundamentals of computer programming in a first course (CS1) is a complex activity for the professor and is also a challenge for them. Nowadays, there are several teaching strategies for dealing with a CS1 at the university, one of which is the use of analogies to support the abstraction process that a student needs to carry for the appropriation of fundamental concepts. This article presents the results of applying a discovery model that allowed for the extraction of patterns, linguistic analysis, textual analytics, and linked data when using analogies for teaching the fundamental concepts of programming by professors in a CS1 in university programs that train software developers. For that reason, a discovery model based on machine learning and text mining was proposed using natural language processing techniques for semantic vector space modeling, distributional semantics, and the generation of synthetic data. The discovery process was carried out using nine supervised learning methods, three unsupervised learning methods, and one semi-supervised learning method involving linguistic analysis techniques, text analytics, and linked data. The main findings showed that professors include keywords, which are part of the technical computer terminology, in the form of verbs in the statement of the analogy and combine them in quantitative contexts with neutral or positive phrases, where numerical examples, cooking recipes, and games were the most used categories. Finally, a structure is proposed for the construction of analogies to teach programming concepts and this was validated by the professors and students.

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Land cover classification using super-vised and unsupervised learning techniques

Cited by 18 publications

References 4 publications

Indirect Recognition of Predefined Human Activities

Indirect Recognition of Predefined Human Activities

Crop Detection Using Time Series of Sentinel-2 and Sentinel-1 and Existing Land Parcel Information Systems

Discovery Model Based on Analogies for Teaching Computer Programming

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