3D DeepObject achieves mapping-level positional accuracy. In the geospatial intelligence space, positional accuracy is as important as precision and recall. Unfortunately, convolutional networks in deep learning are invariant to translation. In other words, the positional accuracy from deep learning object detection is inherently poor. Combining deep learning and 3D model fitting, our 3D DeepObject has the best of both worlds. Deep learning can detect object (a bounding box) with close to human-level accuracy, while 3D model fitting can achieve pixel-level positional accuracy. The output (bounding boxes) from deep learning are the input for 3D model fitting. A bounding box from deep learning can significantly reduce the search space for 3D model fitting. Our latest test indicates that 3D DeepObject can achieve much higher positional accuracy than deep learning or 3D model fitting alone can achieve.