# DeepSSN: a deep convolutional neural network to assess spatial scene similarity

arXiv: https://arxiv.org/abs/2202.04755 · HTML: https://mlsanity.com/paper/2202.04755/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 94 references that are in the map.
- First posted on arXiv 7 Feb 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells](https://mlsanity.com/paper/2003.00824/) (2020)
- [You Are Here: Geolocation by Embedding Maps and Images](https://mlsanity.com/paper/1911.08797/) (2019)
- [Learning to rank in person re-identification with metric ensembles](https://mlsanity.com/paper/1503.01543/) (2015)
- [Incorporating Intra-Class Variance to Fine-Grained Visual Recognition](https://mlsanity.com/paper/1703.00196/) (2017)
- [Learning Incremental Triplet Margin for Person Re-identification](https://mlsanity.com/paper/1812.06576/) (2018)
- [How to Train Triplet Networks with 100K Identities?](https://mlsanity.com/paper/1709.02940/) (2017)
- [Extracting human emotions at different places based on facial expressions and spatial clustering analysis](https://mlsanity.com/paper/1905.01817/) (2019)

## Built on by (most cited first, 2 of 2)

- [Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relations](https://mlsanity.com/paper/2505.17136/) (2025)
- [Unrecognizable Yet Identifiable: Image Distortion with Preserved Embeddings](https://mlsanity.com/paper/2401.15048/) (2024)

## Abstract (arXiv)

Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch-based spatial search methods perform insufficiently due to their inability to find hidden multi-scale map features from mental sketches. In this research, we propose a deep convolutional neural network, namely Deep Spatial Scene Network (DeepSSN), to better assess the spatial scene similarity. In DeepSSN, a triplet loss function is designed as a comprehensive distance metric to support the similarity assessment. A positive and negative example mining strategy using qualitative constraint networks in spatial reasoning is designed to ensure a consistently increasing distinction of triplets during the training process. Moreover, we develop a prototype spatial scene search system using the proposed DeepSSN, in which the users input spatial query via sketch maps and the system can automatically augment the sketch training data. The proposed model is validated using multi-source conflated map data including 131,300 labeled scene samples after data augmentation. The empirical results demonstrate that the DeepSSN outperforms baseline methods including k-nearest-neighbors, multilayer perceptron, AlexNet, DenseNet, and ResNet using mean reciprocal rank and precision metrics. This research advances geographic information retrieval studies by introducing a novel deep learning method tailored to spatial scene queries.