Preface

Feedback from user survey from Latent Space 1.0:

70% of users were confused about why the model was generating certain kinds of drawings.
80% of users had trouble understanding the function of training data and user input in generative models.

Solution

Add a personalized dataset feature that allows users to label personal datasets, and train their own models.

Straightforward data management

01

Upload and manage data with ease

Better workflow and better knowing users’ next step.

Data Visualization

02

Visualized Data Structure

Create tree map to visualize datasets by connecting relations, helping users manage datasets.

Fast onboarding process

03

Simple Tutorial on Web

Users can quickly learn how to train and manage their own data with our simple tutorial on the website to speed up their interactions with data.

Add a key feature

We added a feature to allow user upload and manage their own datasets.

Now, we have divided the product into 3 sectors, focusing on different aspects. Our goal is to set up an ecosystem with these features.

Research Cycle 2

Ideation workshop on Creative Labeling

We conducted an ideation workshop to better understand the logic behind machine learning.

Design decision

01

Organize and visualize data

The data team works on restructuring datasets to obtain user insights. We organized the original data from our collections into drawing formats that are popular in architectural practice. Using graphics and images to convey complex ideas and logic better. We highlight useful information from our data collection and help the users better visualize what they could do.

From Original Dataset To Popular drawing format

Design decision

02

Define simple annotation types

Define three main image annotation types and converted them to visual and presentable formats. This is easier for designers to understand.

From Lines, Polygon, Semantic Segmentation

To Feature Extraction, Plan Layout, Building Segmentation

Design decision

03

Better Interface and Workflow

I focused on designing the interface for users to upload and manage their data.

01 Split view

We use the Split View pattern: side-by-side panes of content for the Data Space interface. It reduces physical effort, visual cognitive load, and the user's memory burden to navigate between windows.

SOLUTION 01

Make visible the model training process

• Many users complimented this progress bar during the workshop. Now users can see which step they are in when training their data to cut down cognitive load and encourage them to stay on the page.

SOLUTION 02

Move the data label to the left pane to give more emphasis

• It's the most important feature for our 2.0 version and how users learn data labeling.

SOLUTION 03

Separate labels and label structure

• Move the data label to the right pane to reduce user confusion and memory burden

02 Color code labels

Adding colors to labels has been one of the top requests of our user interview. We added colors to help users identify different label characteristics easier.