What is the name of your organization?
RIFFAI PTE. LTD.
What is the name of your solution?
RIFFAI
Provide a one-line summary or tagline for your solution.
RIFFAI, AI and Satellite company that utilizes hyper-multi-spectral imaging to monitor environmental changes in Earth Observation.
In what city, town, or region is your solution team headquartered?
Singapore
In what country is your solution team headquartered?
Singapore
What type of organization is your solution team?
For-profit, including B-Corp or similar models
Film your elevator pitch.
What specific problem are you solving?
At RIFFAI, we aim to tackle the escalating social challenges posed by climate change. Our focus lies in addressing the increasing frequency and severity of climate-related disasters, which have risen by 83% in recent decades. $25 Trillion in Net Supply Chain Losses.
Due to climate disruptions, with projections indicating exponential increases as climate change intensifies. RIFFAI wants to be a solution that ends the global challenges in the supply chain with power of satellite and AI on a planetary scale.
We aim to mitigate financial risks tied to climate disasters. Insurance miscalculations have resulted in $121 billion in losses annually, leaving both businesses and individuals unprotected. Our technology supports accurate risk assessments, fostering resilience for economic sectors like agriculture, infrastructure, and insurance.
By providing actionable insights, RIFFAI empowers governments, organizations, and communities to proactively prepare for and adapt to environmental challenges. By harnessing the power of satellites, AI, and cutting-edge engineering, we transform data from above into actionable solutions on the ground.
What is your solution?
RIFFAI, a Satellite & AI company that utilizes hyper-multi-spectral imaging and time series analysis to monitor environmental changes in Earth Observation. RIFFAI's advanced AI-driven models process multispectral and hyperspectral imaging data, capturing information across various wavelength bands of the electromagnetic spectrum. By leveraging a diverse range of satellite databases, geospatial metadata, and pre-trained AI models, RIFFAI optimizes resource allocation while minimizing operational costs.
By employing image matching techniques, RIFFAI can compare current satellite images with historical images. It identifies changes in specific points or patterns, such as rising water levels or the spread of water across previously dry land. This helps in detecting early signs of environmental changes. The model incorporates forecasted weather data. By analyzing these forecasts, RIFFAI can anticipate conditions that are likely to lead to changes, such as rainfall or winds driving water inland.
RIFFAI offers pre-trained AI and satellite-based solutions tailored to meet the specific needs of governments, businesses, and cities. RIFFAI’s Built-to-Suit (BTS) to Universal APIs approach ensures that each solution is designed, developed, and implemented to address unique environmental challenges. As we have more projects and shared traits of new projects, it allows RIFFAI to scale with quality and cost effectiveness.
Who does your solution serve, and in what ways will the solution impact their lives?
RIFFAI is an advanced AI model designed to detect signs of environmental changes by leveraging various data sources and analytical techniques including satellite imagery database, image matching point distribution, and forecasted weather. The model continuously receives real-time satellite imagery and provides up-to-date visual information about the Earth's surface, allowing the environment to be affected.
RIFFAI employs NDVI (Vegetation), NDWI (Water), MNDWI (Normalized Water), LST (Land Surface Temperature) and with over 800+ parameters in detecting environmental changes through satellite imagery and GIS layers. These indices provide a framework for monitoring environmental changes and identifying events with little time window. These indicators also help predict coastal changes, wildfire, and agricultural land.
RIFFAI utilizes Artificial Neural Networks (ANN) such as Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to effectively detect environmental changes from satellite imagery. CNNs excel at analyzing visual data, making them ideal for processing and interpreting the complex patterns in satellite images. ANNs help in recognizing significant changes over time, such as flooding or deforestation while LSTMs analyze the sequence of satellite images over time, capturing trends and predicting future environmental changes based on historical data.
