CSV to JSON-FG

CSV to JSON-FG Converter Online | GeoDataViewer

Quick answer: Convert CSV to JSON-FG online for free. Open your CSV data, inspect the map preview, and download JSON-FG output locally in your browser — no upload required. CSV is a plain text table format that becomes geospatial only when rows include coordinates, WKT, or some other geometry-bearing columns.

Before converting CSV to JSON-FG

Prepare the input

Use a header row and explicit numeric longitude/latitude columns in decimal degrees. Addresses alone are not coordinates. For mixed DMS or reversed columns, clean the table first.

Know what you download

JSON-FG output is downloaded as a file, or ZIP when the driver generates sidecars. Compare the result against the source before sharing.

Download a real CSV test file

Expected result: Three synthetic WGS 84 locations near London with name, latitude and longitude columns. Expect three point features, then compare their names and positions.

Limits and troubleshooting

Converting CSV to JSON-FG can change field types, metadata and presentation. A successful download does not prove every source element survived.
Browser memory varies by device. There is no guaranteed unlimited file size; split large datasets or use a desktop GIS when memory or WASM initialization fails.

If something goes wrong

No points or wrong location: select numeric coordinate columns, check decimal versus DMS and verify longitude/latitude order. Use coordinate cleanup for uncertain input.
Wrong continent or distorted geometry: inspect the source CRS, axis order and a known location. Valid syntax alone does not guarantee valid placement.
Conversion cannot start: reload while online so processing libraries and WebAssembly can load, then retry a smaller known-good sample. File contents are still processed locally.
Incomplete export: compare source/output counts, geometry families and key fields. Inspect warnings and keep the original file.

Source Format Guide

What is CSV?

CSV is a plain text table format that becomes geospatial only when rows include coordinates, WKT, or some other geometry-bearing columns.

CSV is attractive because almost every spreadsheet, BI tool, script, and database can read it, which makes it one of the easiest tabular handoff formats in data work.

In GIS workflows, CSV usually acts as an entry point or export format rather than a full spatial model because it does not natively encode geometry types, CRS metadata, or topology rules.

Common Workflows

Common use cases

Address lists, asset inventories, or sensor records with latitude and longitude columns.
QA exports where analysts need to sort and filter records outside a GIS application.
Quick imports into scripts, databases, cloud notebooks, or low-code data tools.

Ecosystem

Where you will encounter it

Spreadsheet and BI tools.
ETL pipelines, scripts, and cloud notebooks.
Business review workflows that need tabular delivery first.

Strengths

Why teams choose CSV

Very easy to open, inspect, diff, and load into general-purpose tooling.
Lightweight file structure works well for automated pipelines.
Good bridge format when the audience is not primarily using GIS software.

Limitations

Where CSV gets awkward

CSV has no native geometry model, so location must be inferred from columns or embedded text.
CRS, styling, topology, and richer feature metadata are not standardized in plain CSV.
Multi-geometry or multi-layer datasets are better stored in dedicated geospatial containers.

Conversion steps

How to convert CSV to JSON-FG online

1. Add your CSV file

Drop the source data into the converter above. Keep any required sidecar files together when your dataset uses them.
2. Check the map preview

Inspect the geometry and attributes before exporting so you can spot missing layers or coordinate-system issues.
3. Download JSON-FG

Run the conversion and save the generated JSON-FG output directly from your browser. Your source file stays on your device.

File Structure

Common file extensions and sidecar files

.csv

Main comma-separated text table containing rows and columns.

latitude / longitude columns

Common convention for point locations when the CSV is used as geospatial input.

WKT or geometry text column

Optional way to carry non-point geometry in a flat table, depending on downstream tooling.

Conversion Rationale

Why convert CSV to JSON-FG?

Teams commonly convert CSV into JSON-FG when they need a JSON workflow with more geospatial semantics than classic GeoJSON.

Teams convert CSV into GIS-native formats when they need explicit geometry support, CRS handling, or more reliable feature semantics.
CSV exports are also commonly converted into map-ready formats so the same records can be previewed and styled spatially instead of only as rows.
JSON-FG can preserve feature typing, richer CRS handling, and newer geospatial JSON patterns.
It is a useful target when you want JSON ergonomics without being limited to classic GeoJSON assumptions.

FAQ

Frequently asked questions about CSV to JSON-FG

What do I need before converting CSV to JSON-FG?

Use a header row and explicit numeric longitude/latitude columns in decimal degrees. Addresses alone are not coordinates. For mixed DMS or reversed columns, clean the table first.

What exactly is in the JSON-FG download?

JSON-FG output is downloaded as a file, or ZIP when the driver generates sidecars. Compare the result against the source before sharing.

How large a CSV file can I convert?

The practical limit depends on browser memory, geometry complexity and the conversion driver. Start with a small sample. There is no guaranteed unlimited size and no cloud fallback or payment tier for large files.

Are my CSV files sent to a server?

File parsing and conversion run on your device. The page, WebAssembly and map basemaps can make network requests; choosing a file does not upload its contents. Load the required libraries before attempting offline work.

Can I fix DMS, dirty cells or reversed latitude and longitude here?

Use the coordinate cleanup tool to choose the worksheet and columns, parse supported decimal/DMS values, review rejected rows and export KML or GeoJSON. Ambiguous coordinate order needs a known location; it is not silently guessed.

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