simple-features-geojson-java | Simple Features GeoJSON Java Library | Dataset library

 by   ngageoint Java Version: 3.0.2 License: MIT

kandi X-RAY | simple-features-geojson-java Summary

kandi X-RAY | simple-features-geojson-java Summary

simple-features-geojson-java is a Java library typically used in Artificial Intelligence, Dataset applications. simple-features-geojson-java has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.

Simple Features GeoJSON is a Java library for writing and reading Simple Feature Geometries to and from GeoJSON.

            kandi-support Support

              simple-features-geojson-java has a low active ecosystem.
              It has 24 star(s) with 10 fork(s). There are 7 watchers for this library.
              It had no major release in the last 12 months.
              There are 0 open issues and 1 have been closed. On average issues are closed in 418 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of simple-features-geojson-java is 3.0.2

            kandi-Quality Quality

              simple-features-geojson-java has 0 bugs and 0 code smells.

            kandi-Security Security

              simple-features-geojson-java has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              simple-features-geojson-java code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              simple-features-geojson-java is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              simple-features-geojson-java releases are available to install and integrate.
              Deployable package is available in Maven.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              It has 18660 lines of code, 264 functions and 89 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed simple-features-geojson-java and discovered the below as its top functions. This is intended to give you an instant insight into simple-features-geojson-java implemented functionality, and help decide if they suit your requirements.
            • Compares this MultiPoint
            • Compares this object to another
            • Replies the hash code
            • Returns a hashCode of this element
            • Compares two points
            • Compares this position to another
            • Compares this object for equality
            • Gets the properties map
            • Returns a hashCode hashCode
            • HashCode method
            • Returns the hashCode of this map
            • Returns a hashCode of this geometry
            • Generate hash code for this sketch
            • Returns the hashCode of this object
            • Converts simple geometries to feature collection
            • Compare two geometries
            • Get the geometry type
            • Serialize a position
            • Compares two Polygon objects
            • Compares two MultiPolygon objects
            • Compares two MultiLineString objects
            • Returns a hash code
            Get all kandi verified functions for this library.

            simple-features-geojson-java Key Features

            No Key Features are available at this moment for simple-features-geojson-java.

            simple-features-geojson-java Examples and Code Snippets

            Javadot img1Lines of Code : 24dot img1License : Permissive (MIT)
            copy iconCopy
            //String content = ...    
            Geometry geometry = FeatureConverter.toGeometry(content);
            mil.nga.sf.Geometry simpleGeometry = geometry.getGeometry();
            /* Read as a generic GeoJSON object, Feature, or Feature Collection */
            //GeoJsonObject geoJsonObject   
            Javadot img2Lines of Code : 7dot img2License : Permissive (MIT)
            copy iconCopy
            Javadot img3Lines of Code : 1dot img3License : Permissive (MIT)
            copy iconCopy
            mvn clean install

            Community Discussions


            Replacing dataframe value given multiple condition from another dataframe with R
            Asked 2022-Apr-14 at 16:16

            I have two dataframes one with the dates (converted in months) of multiple survey replicates for a given grid cell and the other one with snow data for each month for the same grid cell, they have a matching ID column to identify the cells. What I would like to do is to replace in the first dataframe, the one with months of survey replicates, the month value with the snow value for that month considering the grid cell ID. Thank you



            Answered 2022-Apr-14 at 14:50
            df3 <- df1
            df3[!] <- df2[!]
            #   CellID sampl1 sampl2 sampl3
            # 1      1    0.1    0.4    0.6
            # 2      2    0.1    0.5    0.7
            # 3      3    0.1    0.4    0.8
            # 4      4    0.1      
            # 5      5         
            # 6      6         



            Does Hub support integrations for MinIO, AWS, and GCP? If so, how does it work?
            Asked 2022-Mar-19 at 16:28

            I was taking a look at Hub—the dataset format for AI—and noticed that hub integrates with GCP and AWS. I was wondering if it also supported integrations with MinIO.

            I know that Hub allows you to directly stream datasets from cloud storage to ML workflows but I’m not sure which ML workflows it integrates with.

            I would like to use MinIO over S3 since my team has a self-hosted MinIO instance (aka it's free).



            Answered 2022-Mar-19 at 16:28

            Hub allows you to load data from anywhere. Hub works locally, on Google Cloud, MinIO, AWS as well as Activeloop storage (no servers needed!). So, it allows you to load data and directly stream datasets from cloud storage to ML workflows.

            You can find more information about storage authentication in the Hub docs.

            Then, Hub allows you to stream data to PyTorch or TensorFlow with simple dataset integrations as if the data were local since you can connect Hub datasets to ML frameworks.



            Custom Sampler correct use in Pytorch
            Asked 2022-Mar-17 at 19:22

            I have a map-stype dataset, which is used for instance segmentation tasks. The dataset is very imbalanced, in the sense that some images have only 10 objects while others have up to 1200.

            How can I limit the number of objects per batch?

            A minimal reproducible example is:



            Answered 2022-Mar-17 at 19:22

            If what you are trying to solve really is:



            C++ what is the best sorting container and approach for large datasets (millions of lines)
            Asked 2022-Mar-08 at 11:24

            I'm tackling a exercise which is supposed to exactly benchmark the time complexity of such code.

            The data I'm handling is made up of pairs of strings like this hbFvMF,PZLmRb, each string is present two times in the dataset, once on position 1 and once on position 2 . so the first string would point to zvEcqe,hbFvMF for example and the list goes on....

            example dataset of 50k pairs

            I've been able to produce code which doesn't have much problem sorting these datasets up to 50k pairs, where it takes about 4-5 minutes. 10k gets sorted in a matter of seconds.

            The problem is that my code is supposed to handle datasets of up to 5 million pairs. So I'm trying to see what more I can do. I will post my two best attempts, initial one with vectors, which I thought I could upgrade by replacing vector with unsorted_map because of the better time complexity when searching, but to my surprise, there was almost no difference between the two containers when I tested it. I'm not sure if my approach to the problem or the containers I'm choosing are causing the steep sorting times...

            Attempt with vectors:



            Answered 2022-Feb-22 at 07:13

            You can use a trie data structure, here's a paper that explains an algorithm to do that:

            But you have to implement the trie from scratch because as far as I know there is no default trie implementation in c++.



            How to create a dataset for tensorflow from a txt file containing paths and labels?
            Asked 2022-Feb-09 at 08:09

            I'm trying to load the DomainNet dataset into a tensorflow dataset. Each of the domains contain two .txt files for the training and test data respectively, which is structured as follows:



            Answered 2022-Feb-09 at 08:09

            You can use to load and process multiple txt files at a time:



            Converting 0-1 values in dataset with the name of the column if the value of the cell is 1
            Asked 2022-Feb-02 at 07:02

            I have a csv dataset with the values 0-1 for the features of the elements. I want to iterate each cell and replace the values 1 with the name of its column. There are more than 500 thousand rows and 200 columns and, because the table is exported from another annotation tool which I update often, I want to find a way in Python to do it automatically. This is not the table, but a sample test which I was using while trying to write a code I tried some, but without success. I would really appreciate it if you can share your knowledge with me. It will be a huge help. The final result I want to have is of the type: (abonojnë, token_pos_verb). If you know any method that I can do this in Excel without the help of Python, it would be even better. Thank you, Brikena



            Answered 2022-Jan-31 at 10:08

            Using pandas, this is quite easy:



            How can i get person class and segmentation from MSCOCO dataset?
            Asked 2022-Jan-06 at 05:04

            I want to download only person class and binary segmentation from COCO dataset. How can I do it?



            Answered 2022-Jan-06 at 05:04


            R - If column contains a string from vector, append flag into another column
            Asked 2021-Dec-16 at 23:33
            My Data

            I have a vector of words, like the below. This is an oversimplification, my real vector is over 600 words:



            Answered 2021-Dec-16 at 23:33

            Update: If a list is preferred: Using str_extract_all:



            How to divide a large image dataset into groups of pictures and save them inside subfolders using python?
            Asked 2021-Dec-08 at 15:13

            I have an image dataset that looks like this: Dataset

            The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).

            Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs). The result would ideally look like this: Sequences

            I have tried using os.walk and loop inside the folder where the image dataset is saved, then I created a dataframe using pandas because I thought I could handle the files more easily but I think I am totally off target here.



            Answered 2021-Dec-08 at 15:10

            The timestep of each image is 15 minutes (as you can see, the timestamp is in the filename).

            Now I would like to group those images in 3hrs long sequences and save those sequences inside subfolders that would contain respectively 12 images(=3hrs)

            I suggest exploiting datetime built-in libary to get desired result, for each file you have

            1. get substring which is holding timestamp
            2. parse it into datetime.datetime instance using datetime.datetime.strptime
            3. convert said instance into seconds since epoch using .timestamp method
            4. compute number of seconds integer division (//) 10800 (number of seconds inside 3hr)
            5. convert value you got into str and use it as target subfolder name



            Proper way of cleaning csv file
            Asked 2021-Nov-15 at 22:58

            I've got a huge CSV file, which looks like this:



            Answered 2021-Nov-15 at 21:33

            You can use a regular expression for this:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install simple-features-geojson-java

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            Build this repository using Eclipse and/or Maven:.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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