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l5d1l5's Projects

cmip_work icon cmip_work

Process and Analyze CMIP data for downscaling

cmipcarbon icon cmipcarbon

Reproducible work flow that processes CMIP6/5 carbon output as part of two projects.

cmiplib icon cmiplib

Libraries for use in CMIP data indexing and manipulation

cnn_ucmerced-landuse_caffe icon cnn_ucmerced-landuse_caffe

Based on the deep learning framework (Caffe), the classification of the optical remote sensing image UCMerced LandUse dataset was completed.

cnns-for-wind-field-downscaling icon cnns-for-wind-field-downscaling

Code repository for the paper "A Comparative Study of Convolutional Neural Network Models for Wind Field Downscaling" by Kevin Höhlein, Michael Kern, Timothy Hewson and Rüdiger Westermann

coastalrisk icon coastalrisk

Estimates the impact of water risks on crop production in 6 coastal Bangladesh polders

coexistenceholes.jl icon coexistenceholes.jl

Website for the paper "Coexistence holes characterize the assembly and disassembly of multispecies systems"

colombia-marxan-analysis icon colombia-marxan-analysis

This program is an example of how a Marxan Analysis can be utilized to aid in biodiversity conservation efforts. Marxan is an optimization program that selects areas (planning units) based on a variety of parameters including but not limited to cost of land, threat level to species, and spatial relationships.

comola icon comola

CoMOLA is a generic python tool for Constrained Multi-objective Optimization of Land use Allocation. It offers a framework to explore a landscape’s potential for ecosystem service supply and biodiversity conservation considering land use conversion and areal coverage constraints. CoMOLA can be used immediately by inputting a raster map representing the status-quo land use, ready-to-run models written in R including their input data, and (optional) information on constraints. The end product is a set of pareto-optimal solutions (land use maps) from which decision makers can discuss and select appropriate solutions according to their preferences.

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