#dronedata

From Drones to Docks: Precision Annotations for Smarter Seas

We translate drone vision into structured intelligence. Wisepl specialize in meticulously crafted data annotation that powers AI to detect, classify, and track ships with unmatched accuracy.

Whether it’s identifying cargo vessels in vast oceans or spotting fishing boats near coastlines, our expert annotators draw every pixel with purpose - fueling machine learning models for maritime surveillance, defense, logistics, and climate studies.

What sets us apart?

High-precision polygon & bounding box annotations

Expertise in drone-captured geospatial datasets

Scalable, secure & quality-checked pipeline

Human-in-the-loop workflows with smart reviews

We don’t just label ships | We empower algorithms to understand the sea.

LiDAR sensors mounted on aircrafts or drones are becoming widely used for mapping.  And they are often perceived as a competing technology with standard cameras.  It is quite the opposite as they in fact complement each other by capturing data of different nature.  Merged together, they actually lead to great advantages for carrying information about the terrain.  While LiDAR describes the topography at a high resolution, imagery captures the appearance of ground features.  Thus, to integrate these two sources of information usually means using the images to colorize the LiDAR point cloud.  It can also imply using the LiDAR as elevation data to generate orthophotos.

The main challenge in integrating LiDAR with imagery is to achieve an accurate co-registration.  In other words, both datasets must be perfectly aligned together.  The traditional approach is to use ground control points (GCPs) to ensure absolute accuracy separately in both datasets.  When subsequent fusion is done, then alignment of the same ground features is accomplished.  A major drawback in that method is that the GCPs must be manually tagged twice i.e., in the LiDAR and then in the imagery.

A significantly more efficient method removes the need to control the imagery with GCPs.  Instead, the LiDAR data is directly used as a reference and the imagery is automatically registered through bundle adjustment.  Tie points are first created by matching corresponding spatial features from the imagery to the LiDAR intensity map.  Overall, that new approach allows to quickly combine LiDAR with data from camera systems and produce high-quality outputs.

Artificial Intelligence – The Holy Grail of Computing

Artificial Intelligence is the holy grail of computing. Learning through highly sophisticated Deep Neural Networks (DNN) is the critical ingredient enabling machines to be so intelligent that they learn on their own. In this neural learning environment a perceptionis being trained for object recognition and classification. Already academia, scientists and a broad spectrum of industries recognise…

Artificial Intelligence is Impacting our Workplace and Everyday Lives

Artificial Intelligence is the Apex technology of the information age and will impact absolutely everything in our workplace and lives.  Goldman Sachs Researchers believe that “Today’s machine self-learning capability is the most far-reaching and significant advancement of the 21st century”.

Simply put, our world today is moving beyond people instructing machines to operate by…

Monetizing Data from 600,000 Commercial Drones

There will be an estimated 600,000 commercial drones operating in the U.S. within the year as the result of new safety rules according to FAA Administrator Huerta. US Transportation Secretary Foxx says  “We are in one of the most dramatic periods of change in the history of transportation“.

Its common knowledge that the use cases for commercial drones is enormous. The first wave has been…