Do you want to publish a course? Click here

PyLUSAT: An open-source Python toolkit for GIS-based land use suitability analysis

234   0   0.0 ( 0 )
 Added by Changjie Chen
 Publication date 2021
and research's language is English




Ask ChatGPT about the research

Desktop GIS applications, such as ArcGIS and QGIS, provide tools essential for conducting suitability analysis, an activity that is central in formulating a land-use plan. But, when it comes to building complicated land-use suitability models, these applications have several limitations, including operating system-dependence, lack of dedicated modules, insufficient reproducibility, and difficult, if not impossible, deployment on a computing cluster. To address the challenges, this paper introduces PyLUSAT: Python for Land Use Suitability Analysis Tools. PyLUSAT is an open-source software package that provides a series of tools (functions) to conduct various tasks in a suitability modeling workflow. These tools were evaluated against comparable tools in ArcMap 10.4 with respect to both accuracy and computational efficiency. Results showed that PyLUSAT functions were two to ten times more efficient depending on the jobs complexity, while generating outputs with similar accuracy compared to the ArcMap tools. PyLUSAT also features extensibility and cross-platform compatibility. It has been used to develop fourteen QGIS Processing Algorithms and implemented on a high-performance computational cluster (HiPerGator at the University of Florida) to expedite the process of suitability analysis. All these properties make PyLUSAT a competitive alternative solution for urban planners/researchers to customize and automate suitability analysis as well as integrate the technique into a larger analytical framework.



rate research

Read More

Fermipy is an open-source python framework that facilitates analysis of data collected by the Fermi Large Area Telescope (LAT). Fermipy is built on the Fermi Science Tools, the publicly available software suite provided by NASA for the LAT mission. Fermipy provides a high-level interface for analyzing LAT data in a simple and reproducible way. The current feature set includes methods for extracting spectral energy distributions and lightcurves, generating test statistic maps, finding new source candidates, and fitting source position and extension. Fermipy leverages functionality from other scientific python packages including NumPy, SciPy, Matplotlib, and Astropy and is organized as a community-developed package following an open-source development model. We review the current functionality of Fermipy and plans for future development.
Vector-based cellular automata (CA) based on real land-parcel has become an important trend in current urban development simulation studies. Compared with raster-based and parcel-based CA models, vector CA models are difficult to be widely used because of their complex data structures and technical difficulties. The UrbanVCA, a brand-new vector CA-based urban development simulation framework was proposed in this study, which supports multiple machine-learning models. To measure the simulation accuracy better, this study also first proposes a vector-based landscape index (VecLI) model based on the real land-parcels. Using Shunde, Guangdong as the study area, the UrbanVCA simulates multiple types of urban land-use changes at the land-parcel level have achieved a high accuracy (FoM=0.243) and the landscape index similarity reaches 87.3%. The simulation results in 2030 show that the eco-protection scenario can promote urban agglomeration and reduce ecological aggression and loss of arable land by at least 60%. Besides, we have developed and released UrbanVCA software for urban planners and researchers.
215 - Zhe Zhao , Hui Chen , Jinbin Zhang 2019
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training model that remains unexplored. With UER, we have built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets (objectives). With proper pre-trained models, we could achieve new state-of-the-art results on a range of downstream datasets.
Familia is an open-source toolkit for pragmatic topic modeling in industry. Familia abstracts the utilities of topic modeling in industry as two paradigms: semantic representation and semantic matching. Efficient implementations of the two paradigms are made publicly available for the first time. Furthermore, we provide off-the-shelf topic models trained on large-scale industrial corpora, including Latent Dirichlet Allocation (LDA), SentenceLDA and Topical Word Embedding (TWE). We further describe typical applications which are successfully powered by topic modeling, in order to ease the confusions and difficulties of software engineers during topic model selection and utilization.
Textual adversarial attacking has received wide and increasing attention in recent years. Various attack models have been proposed, which are enormously distinct and implemented with different programming frameworks and settings. These facts hinder quick utilization and apt comparison of attack models. In this paper, we present an open-source textual adversarial attack toolkit named OpenAttack. It currently builds in 12 typical attack models that cover all the attack types. Its highly inclusive modular design not only supports quick utilization of existing attack models, but also enables great flexibility and extensibility. OpenAttack has broad uses including comparing and evaluating attack models, measuring robustness of a victim model, assisting in developing new attack models, and adversarial training. Source code, built-in models and documentation can be obtained at https://github.com/thunlp/OpenAttack.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا