CELSO Data Science

CaseBineFacute.ro

Main input: This project aims to gather constructions plans made by architects - official blueprints.

This format is far more reliable than the ones available on the internet (2d/3d floorplans) because it`s normalized according to architectural standards.

Derivatives: Through this project we were able to gain a deeper insight in the construction and real estate industry, thus paving the way to future projects like eDezvoltator.

Sustainability: 100% self-sustained. The website generates about 1M EUR yearly revenue for a construction company based in Bucharest. We collect a small percentage of their income.

Transfagarasan.travel

Main input: Through this travel website we gathered more than 1000 panoramic images from various locations (hotels, restaurants etc).

Panoramic images because they contain a far more natural perspective than still images, thus allowing us to train models in ways closer to natural learning than with usual image data sets.

Derivatives: As a secondary layer of data, we scraped almost 2M images from social media with real people at various events (like a party). We will use these images to refine sentiment classification models.

Sustainability: ~70% self-sustained. In oder to gather the the panoramic images, we had to send field teams that visited each location and took the photos with specialised hardware (Ricoh THETA).

These teams were partially financed by a small sum that we collected from the respective locations in exchange for the materials delivered (photos and virtual tours).

In the nearby future, we aim to monetize this project through a voucher mechanism.

Image segmentation

We consider that, for real-life usage of machine learning models, it`s critical to segment the image before initialising any other form of classification.

This is very close to how nature solved this problem - our brains focus on a part of the scene before starting to react to it, often times being almost oblivious to the rest of the scene.

Successful segmentation will greatly reduce resource consumption and output quality by threading various parts of the image through specialised models.

For example, a certain scene may include people dancing and people sitting at tables. The table scene can be passed through a model trained on objects like tableware or food with the primary aim to detect objects, while the dancing scene can be sent to a model trained on concerts and events with the primary aim to detect sentiment.

This way, the machine will not only build precise interpretations of the scene but also make sense of the context in which the scene takes place, similar to how our brain connects different parts of information in order to create holistic understanding.

We use both simple and complex scenes to train our models in a multi-step succession. The first layer aims to identify the type of scene from a known index by looking for features (for example bed + nightstands + tv is 90% bedroom), then is passes the scene to the next layer which is a specialised one (that was trained with bedroom objects) and does most of the computing.

The first layer computes as little as possible of the scene. It`s main role is feeding the lower layers in a thread based system - so it does some resource allocation and prioritization.

Object detection

Nature combines input from various sensors to classify objects.

For example, we use touch AND vision to build a mental model of a toy dog by rotating and examining it from various angles.

In order to partially replicate this nature`s ways, we decided to use panoramic images and 3D virtual tours that capture the scenes from more than one viewpoint.

This way, we detect objects using "dynamic features" - features observed from different perspectives, thus greatly increasing the model`s accuracy.

Sentiment analysis

This is mostly a secondary activity. We don`t assign it as much resources as to the other two fields described before.

Combined with image segmentation, we aim to predetermine the mood of each person in the scene based on context, not just on facial expression.

eDezvoltator.ro

Main input: This project uses two main data sets, representing a segment of the real estate market in Romania - new buildings.

The first set is related to the housing offer and it contains more than 300 data points collected from more than 700 real estate projects, mostly in Bucharest.

The "demand" data-set includes information collected from the users of the website. This data is generated by a series of triggers scattered through the platform. We measure everything from macro-interactions (such as choosing price-range) to micro-interactions (such as time spent looking at a certain part of the page).

Derivatives: In order to prepare the content published on the website, we require the real estate developers to provide technical drawings of their buildings. Besides creating content, we use these drawings to train the machine vision models described in the previous section.

Sustainability: ~90% self-sustained. The platform generates leads which we then monetize through a CPL or commission-based mechanism.

Data Labelling

We try to identify repetitive patterns in which the users react to certain features of the offer.

Labelling is done according to an internal code syste. Then, the data is passed through normalisation procedures in order to eliminate any irregularities.

Data Visualization

The normalised data is plotted using various chart models and examined by real estate professionals that look for correlations between the plotted behaviour.