The good news is that Timelase is being used in very large organizations with many analysts across a variety of projects containing millions of images. However, it does require some knowlege of how to work around several Timelapse limitatons. Read on.

Background: Limitations of cloud-based solutions

An ‘ideal’ system would be one where a person could just log onto it and do their work, with the system taking care of multiple users, conflict resolution (e.g., several people working on the same images at the same time), storage, software updates, and so on. This would normally be built as a cloud-based centralized system accessed via the internet. While convenient, centralized systems have limitations that can affect your work.

1. They are complex to build, often requiring a systems person to set up and maintain.
2. They run into the usual internet problems, such as:
   • reviewing and tagging high resoution images may be slow, as each image must be downloaded;
   • working in remote locations with poor or sporadic internet connections is problematic and frustrating;
   • working offline is usually not possible.
3. They usually impose a particular workflow and are often inflexible, for example:
   • tagging is constrained to a few fixed fields;
   • the workflow may not match what you are doing;
   • some operations may not be possible;
   • may require extra steps or work that you don’t really need for your task.
4. If it is owned or hosted by a 3rd party:
   • they may charge you;
   • there could be privacy or security concerns behind uploading your images/data to their site;
   • there are no guarantees the 3rd party will be around in a few years (or that the system will be maintained);
   • changes may occur that could hinder your future work.
5. No one size fits all system is currently available:
   • some organizatons appear to be working on flexible systems, but I am not aware of a clear winner.

Why Timelapse is a stand-alone application

Timelapse was developed as a stand-along applicaton to get around some of the above issues.

1. It is optimized for rapid tagging (virtually everyone who uses Timelapse says so).
2. It provides significant flexibility – you get to decide the data fields you want, how they are labelled in the user interface, the various help messages, and more.
3. It lets you develop your own workflow to fit your working style and needs.
4. It uses a simple file/folder structure to define an image set, i.e., a folder containing subfolders with images. Image set files:
   • can be located on your local computer, portable hard drive, an agency’s file server, etc;
   • can be copied or moved and it all still works;
5. Users can download the program and run it on their local machine:
   • installation is easy (caveat: a locked down machine may require your systems person to provide permission)
   • getting up and going is fast;
   • no internet is necessary to run it;
   • it also runs within a virtual machine or network server.
6. Tagging data can be exported as CSV files, and then analyzed, imported into another system.
7. Timelapse has been used by very large organizations with many analyzers and millions of images.

Timelapse limitations and workarounds

Of course, its all about tradeoffs. While Timelapse has limitations, there are ways to work around them.

1. It only runs in Windows.
   • Workaround: use a virtual machine to run it on an Apple Computer or Linux system, or even via the cloud through remote access.
2. While multiple users can access a Timelapse database file (e.g., if its stored on a network server), conflicts can occur when people try to alter the same data.
   • Workaround: Assign different folders to different analysts to process. Timelapse contains a merge facility. You can use the checkout option to create a subset of your project that can be handed out to analysts. (e.g., to analyze images from a particular camera) and the checkin option to bring the results back into your main Timelapse repository. Because these just create folders and files, they can be easily packaged (e.g., as a zip file) and emailed to analysts for processing (or accessed via a cloud-based download site such as Dropbox, OneDrive, etc.).
3. It can slow down somewhat after a million or so records (but only on certain functions).
   • Workaround: Using the merge facility, you can create a master database that contains everything. When analyzing or working on subsets of images, you can use the same checkout/checkin facilities to create smaller subsets with better performance characteristics.
4. Its up to you on how you want to organize your folders and files.
   • Workaround: This is both an advantage and disadvantage. Creating your own folder hierarchy can reflect how you actually want to organize your images and data. However, it does requires some thought. For example, a typical structure organizes images within a Project /Camera Stations / Deployments folder structure, with images located in each Deployment folder (i.e, images retrieved from a particular camera’s SD card).

The above strategies do work. However, they require discipline and thought in: how folders and files are organized as image sets; how image subsets are provided to analysts, and a strategy for retrieving the analysts’ data.

Need more information? See the Folder and files and the Merging database files for large projects sections in the Timelape Reference Guide.