MARS - A Foundational Map Auto-Regressor
Authors: Qi Zhang
Suvam Bag
Rupanjali Kukal
Mikael Figueroa
Rishi Madhok
Nikolaos Karianakis
Fuxun Yu
Venue: ICLR 2026 Poster (2026)
Keywords: Computer Vision
Remote Sensing
Geospatial AI
Human-in-the-loop
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Map generation tasks feature extensive non-structural *vectorized data* (e.g., points, polylines, and polygons) and thus pose significant challenges to common pixel-wise generative models. Conventional approaches use multiple stages, first segmenting these features at the pixel level and then performing vectorized post-processing, with errors and complexity compounding at each stage. Motivated by the recent success of auto-regressive language modeling, we propose the first map foundation model, named Map Auto-Regressor (MARS), that is capable of generating both multi-polyline road networks and polygon buildings in a unified manner. For training MARS, we collected to our knowledge the largest multi-class map extraction dataset totaling 3.4M examples, which we call MAP-3M. Across four road and building datasets, MARS outperforms or matches the performance of multistage baselines. Additionally, we develop a ``Chat with MARS'' capability that enables interactive human-in-the-loop map generation and correction, supported by the auto-regressive nature of our end-to-end approach.
We release our MAP-3M dataset and project demo page at (1) https://huggingface.co/datasets/bag-lab/MAP-3M and (2) https://huggingface.co/spaces/bag-lab/MARS, respectively.

Published as a conference paper at ICLR 2026
MARS A FOUNDATIONAL MAP
Qi Zhang" Suvam Bag*, Rupanjali Kukal Mikael Figueroa, Rishi Madhok, Nikolaos Karianakis, Fuxun Yu Microsoft q zaazg55mmmmm wtt.emimcosoo...ommmmmmoooomo.
ABSTRACT
Map generation tasks feature extensive non-structural vectorized data (c.g., points, polylines, and polygons) and thus pose sigmificant challenges to common pixel- wise generative models. Conventional approaches use multiple stages, first seg- menting these features at the pixel level and then performing vectorized post- processing, with errors and complexity compounding at each stage. Motivated by the recent success of auto-regressive language modeling, we propose the first map foundation model, named Map Auto-Regressor (MARS), that ca- pable of generating both multi-polyline road networks and polygon buildings unified manner. For training MARS, we collected our knowledge the largest multi-class map extraction dataset totaling 3.4M examples, which we call MAP-3M. Across four road and building datasets, MARS outperforms or matches the performance of multistage baselines. Additionally, develop "Chat with MARS" capability that enables interactive human-in-the--oog, map generation and correction, supported by the auto-regressive nature of end- to-end approach. We release our MAP-3M dataset and project demo page at (1) htttt:://hccgingggeeeeee http://iiggaaaa....aaaaatt//t/// httpp::/hugging: http:///aagaaaaooooooooooo///aaa 1ab lab / /MARS respectively.
co/ datasets
1ab/ MMAP-MPP--0 3M and (2)
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INTRODUCTION
Automatically generating maps from overhead or remote-sensing imagery involves converting ras- terized pixels to vectorized geometric primitives including points, polylines, and polygons, which represent diverse map elements such roads, buildings, and water bodies. A key challenge for map generative modeling lies in the vectorized representation of maps (Congalton 1997 Diang et al., 2024). Most current visual generative tasks rasterized, i.e., producing pixel grids, leveraging off- Model (SAM: Kirillov al. 2023) and diffusion models (Rombach et al. 2022). In contrast, map elements are geometric, vector shapes, often consisting of a variable, unstructured set of points, polylines, and polygons, as shown in Fig.
the-shelf tensor-based generative architectures such as the Segment-Anytnn
[ (c). This structural mismatch poses unique difficulties for generic map generative architectures.
As workaround, many papers tackle map generation by first applying rasterized segmentation and then vectorized post-processing in multistage pipeline (Xu et al. (023). For example, SAM plus post-processing methods have been explored for road network graph extraction (Kirillov al. 2023
Hetang et al. 2024). Similarly, Wang et al. (2024) use SAM and design different post-processing logic for building delineation.
Such methods suffer from two major drawbacks that prevent them from serving as foundation model for map generation: (1) limited generality across map classes and (2) suboptimal perfor- mance. For the former, post-processing (e.g., key-point and edge extraction) is typically heuristic- based, and the heuristics differ substantially between feature classes, e.g., road networks (multi- polylines with complex intersections) versus buildings (non-overlapping polygons). As result, these methods generally support only a single map feature type. For the latter, because the gener- ation is not learned end to end within unified architecture, performance is often constrained. It

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Published as a conference paper at ICLR 2026
also leads more hyperparameters. such as NMS IoU thresholds for key-point de-duplication or confidence thresholds for edge connections.
In this work, address map generation tasks by treating vectorized map primitives a formal language. Our approach inspired by auto-regressive architectures have demonstrated effectiveness in producing complex non-structural outputs. Specifically, we design a novel unified map--oo-equenns framework to convert all vectorized map primitives (roads and buildings) into sequential language representation. Then, we propose an end-to-end map culo-regressor (MARS) architecture to conduct Sequence-to-sequeee learning. To ensure generalization and scalability, we do not deviate from conventional standards for the MARS architecture and use only a transformer-basee vision encoder and an auto-regressive decoder, without any intermediate or post-proceseee
learning (Radford et al. 2018), where
map primitive
being applied.
Without bells and whistles, MARS learns to generate all types of map elements at scale. To the best of our knowledge, this is the first map foundation model capable of generating both vectorized roads and buildings within single end-to-end model without any post-processing, marking important step toward scalable and generalizable map generation.
Benefiting from the auto-regressive nature of our model, we further identify emergent prompl- following capability of MARS, like that of GPT (Radford et al.) (018). We then develop "Chat with MARS" feature, where human users can prompt MARS by giving starting point of missing street or a building, which MARS will then auto-complete. This auto-completion of target objects enables new interactive map generation capability: human--n-the-lon map generation and cor- rection.
Training scalable foundation models relies large-scale scale data, but all current map datasets are both single class and limited in quantity. We thus curate MAP-3M, the largest dataset for map gener- ation to date. This collection contains three million images with high-quality annotations for both roads and buildings, 10x x larger in the number of images and 100x broader in geographic coverage than common benchmarks such as Cityscale (He al. 2020a), SpaceNet3 (SpaceNet 2018) and AiCrowd AICrowd 2020).
Overall, our main contributions are as follows:
We proposed foundational map nuto-regressive model architecture: MARS, that can end- to-end generate various vectorized map elements without any post-processing.
We identified and developed "Chat with MARS" that enables brand-new interactive human- in- in-the-loop map generation and correction capability.
We curated by- the largest multi-class map generation dataset to facilitate the founda- tional MARS training. Dataset has been released.
We conduct extensive benchmarks on MAP-3M and various downstream applications. Experimental results demonstrate the superior performance of our unified architecture compared to previous state- of-the-art models, laying the foundation for future advancements in map generation.
(a) Input Image
(b) Rasterized Output
(c) Vectorized Output
Figure
Examples rasterized and vectorized map generation. Source: MARS.
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2 THE MARS FRAMEWORK
Our MARS framework consists of two main parts: a map-lo-sequennce algorithm that transforms vee- tor data into sequential format, and end-to-end map auto-regressor architecture, which includes a vision backbone and an auto-regressive transformer connected with cross-attention.
2.1 MAP-TO-SEQUENCE CONVERSION
To start with, all map objects (building, roads, water bodies, etc.) can be categorized into three basic types: Point, Polyline, and Polygon. For each object, they can be represented by series of vertices and/or edges. For example, point be represented as tuple of its coordinates: [z. y] A polyline can be then represented as a sequence of points: [I1, V1, 22, Ma, 43, ya, 24, V/. ...Js where æ4, Yi is the i-the vertex's coordinates. A polygon is closed- polyline, in which the sequence ends with the starting vertex, e.g., [21, y1, I2, U2, T3, Va. T4, U4, 01, V1
With above sequential representation for single object, representing multiple Polygons without in- tersections such as multiple buildings can be easily formulated as below:
N B, a1 vi. 22 wa vi. ... v1 B, B,.., B. vi. T2 32 33. Vs ri.
B N xI u I3 y3 2.....i
xi v
where B class token for building objects, denotes the i-th building, and 2², is the j-th vertex's s coordinates in the i-th building, and N is the number of buildings.
Representing intersectional Multi-PPlyliine road net- works as a sequence poses the greatest challenge, they form highly complex graphs, with intersections, merges and roundabouts, shown in Fig. (b). This poses many challenges for standard graph traversal methods (Christofides 1973) to account for the semantic continuity and/or separability of those roads.
G6 Original Image
(b) Vanilla Road Networks (Nom Sequemizl)
(c) Simnke-Decompeeet Road Neworlss (Saquentia)
Figure 2: Our stroke-based algorithm decompose non-sequential flat road networks to multiple single polylines, which can then be se- quentialized for auto-regressive learning. Source: MARS.
To sequentialize such road graphs, we adopt a stroke-based algorithm (Yan et al. 2024). This algorithm essentially decom- poses all road segments by intersection points (which have edge degree >3), and then merge consecutive segments within certain angle tolerance (c.g., < 30% to be single road. This simpli- fies complex multi-polyline networks into multiple single-polyline roads that align with real- world road definitions, as shown by different colored single polylines in Fig. 2 (c). The multiple single- polylines be then represented using the same formulation as Eq. with the specialized token replaced by R to denote roads.
With this sequuee- framework, thus enable converting all map elements sequences (points, polygons, multi-polylines). With multiple object types on the map, we just need to construct different class tokens, and then combine them into the final sequence:
P, P....v
B. vi, 222 22, .1., V2. 2) yi=
R, xt, vf. 2h U2. 2.. vs. W, Vi N v 23 V3
21 .1
where P, W are the specialized token for Points, Buildings, Roads, Waterbodies, etc., and i denotes the i-th object, and 21,25 v is the j-th vertex's coordinates in the i-th object. By map- to co-sequence framework, we can convert a vectorized map tile as a sequence, which enables auto- regressive end-to-end map learning without any manual designed vectorized post-processing.
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11....0 AW aataado maddd SEP, P. SEP... 1E0S
Visual Enceder
Auto Regressive Transformer Decoder
"Prompl missing obyce! starting at (El, WTl)
Polyline (road)
49
Sor -oa <arrs, ars, 2f.2f sspps...
(uiiliigg
Label
Figure 3: MARS unifies vectorized map generation by end-to-end visual rugo-regressive modeling. We adopt Swin Transformer vision encoder extract visual context features. The auto-regressive transformer cross-attends visual context features with map tokens, and then generates the sequences (points, polylines, polygons) in an auto-regressive manner. Benefited from teacher-forced forced training, we can also support numan-in-the--e map generation with prompts. Image source: MARS.
2.2 MAP AUTO-REGRESSON ARCHITECTURE
Figure 3 shows the MARS architecture composed of two major parts: (1) vision backbone to extract the visual features, and (2) auto-regressive transformer for vectorized map generation.
The vision backbone (e.g.. Swin-L (Liu al., al., (021)) used to process the satellite imagery and fuse visual context feature to the autoregressive decoder. To pass the multilevel features from the encoder to the decoder, we design a minimal convolutional feature aggregator to concatenate features from four hierarchical levels of the backbone with adaptive upsampling. A feature bridge flattens the visual features and adopts cross-attention to fuse the image context feature into the decoder.
Built vanilla transformer (Radford et al. 2018), the decoder generates the semantic class to- kens and coordinate tokens of each map object auto-regressively by using the encoder's visual con- text features together with previously generated tokens. It uses single-directional causal attention, ensuring that each token attends only to past tokens. In the training phase, the autoregressive de- coder is trained by teacher forcing (Radford et al. (018). For optimization, we apply standard cross-entropy loss to supervise the learning of both semantic class tokens and coordinate tokens. Additionally, we sort all map objects by their distance to the image centroid, and when distances are similar, we sort them by their clockwise angle. This creates a consistent spiral ordering across the dataset and ensures reproducible decoding. By jointly supervising labels and discretized coor- dinates under the same loss function, the model learns to generate complete map object sequences consistent and unified manner. inference phase, the decoder autoregressively predicts tokens from start-of-sequence token until an end-of-sequence token.
To represent both semantic categories and spatial positions in a unified manner, we construct a shared decoding vocabulary D E RB.+B. where B, denotes the number of semantic classes in the ontology and B. denotes the number of pixel locations along the image height/width dimensions share the coordinate tokens for the width (x) and
(e.g., 224). For simplicity and consistency, height (y) dimensions. The shared decoding vocabulary D further extended to include special tokens, such the start-of-sequenat token, separator token, pad token, and end-of-sequence token.
Chat with MARS: HUMAN-IN-THE-LOW MAP GENERATION
With the autoregressive leacher-forcing training, the prediction of each token is conditioned on all preceding tokens. Prompt following capability thus emerges in MARS, which naturally allows user interventions to be seamlessly integrated into the decoding process. We thus develop "Chat with MARS" capability, an interactive numan-in-the-le
paradigm for collaborative map generation.
Chat-with-Man MiRti integrates user prompts through three complementary modes of interaction: start- of-sequence chatting, mid-sequence chatting, and end-of-sequence chatting. These modes enable
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diverse forms of intervention during decoding, allowing to indicate missing object, correct drifting errors, and enhance performance in complex geospatial scenes.
3.1 START OF SEQUENCE CHATTING
Start-of-sequence (SOS) chatting aims provide the first map element's starting point so that MARS generates maps with a better conditioned starting point. As shown in the highlighted part in Eq. 3 one class token and two coordinate tokens (z, y) from one user click could be passed in as prompt to better guide the following auto-regressive token generation:
(SOS),
22 1/2 F3. 2..,..
21. vt. " (EOS)]
This is particularly helpful when test image is extremely blurry out-of-domain: once the first vertex predicted by MARS is ill-conditioned, due to eπror accumulation of auto-regressive nature, the whole sequence may suffer from less detections. In such case, SOS chatting can greatly improve the full image prediction performance. Visualizations could be found in Fig. (a-d).
3.2 MID OF SEQUENCE CHATTING
Mid-of-sequence (MOS) chatting aims to intercept MARS's prediction sequence when it drifts from the desired trajectory, which is common in vectorized road generation. As shown in the highlighted part Eq. two coordinate tokens (z,, y) from one user click could prompted to replace the old drifting tokens and redirect the following road generation:
((SOS), R, v/, 22, v2. *otd yord
Va
(EOS)).
This is particularly helpful when certain predictions an image needs to adjusted. Visualizations could be found in Fig. A (e-h). For minimal impact, we enforce the newly generated tokens to be within that object (i.e., stop when hitting next object class token) without affecting other objects.
3.3 END OF SEQUENCE CHATTING
End-of-sequence (EOS) chatting aims to augment MARS's prediction when there are objects missed from the final predictions, which common for various small map elements. As shown in the highlighted part in Eq. we remove the (EOS) token, and then prompt with new object class token and coordinate tokens (I, y) so as to resume the map generation:
(SOS), R, 11 vi 22 V/2 Tzg. 3/3
(EOS), B
(EOS)).
(5)
Such EOS chatting could be used improve recall. Visualizations could be found in Fig. 4 (i-l).
Chat-with-MARS can extended with mixed forms, easily forming multi-round conversations for interactive editing. We believe it has strong potential for wide range of real-world localized map editing and maintenance tasks such as OSM Change Analyzer (OpenStreetMapp n.d.).
MAP-3M: A LARGE-SCALE MAP DATASET
For map generation, most widely-used benchmarks in the literature cover only single-class anno- tations with limited image quantities, significantly restricting their utility for real-world tasks. For example, Cityseale (He et al. (020a) and SpaceNet3 (SpaceNet (018)) exclusively contain road annotations, while Al-Crowd (AICrowd 2020) focuses solely building annotations.
To overcome these constraints, we curated MAP-3M, the largest high-resolution aerial image map dataset date, comprising approximately 3M high-resolution images (10x bigger than the other available datasets) enriched with high-quality annotations for both buildings and roads. An comparison between MAP-3M and other literature datasets is shown in Table
Images. MAP-3M images are sourced from the National Agriculture Imagery Program (NAIP; U.S.
Department of Agriculture] (025) and were accessed via Microsoft Planetary Computer. Leveraging population data (United States Cities Database 2022), we evenly sample 5,000 cities from 50 states. More details are in Appendix. A.1 A.1
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(b)
(0)
(h)
Figure 4: Chat with MARS. The user interactions are provided as single clicks, shown as yellow arrows. White dash-dotted line: Ground truth. Colored lines with asterisks: Predicted sequences. Black circles with white numbers: Object IDs. (a-b): Sfart-of-sequence interaction enables recovery of missing building object (ID) 77) in a NAIP image. (c-d): Similarly, a missing road object (ID 1) is recovered in a Cityscale image. (e-f): Mid-sequence interaction helps correct the prediction of drifting road object (ID 10) in Cityscale. (g-b); Another mid-sequence example shows recovery of drifting road segment (ID 3) in Cityscale. (i-j): End-of-sequence interaction allows the model to recover missing road object (ID 6) a NAIP image. (k-l); End-of-sequence interaction recovers missing building object (ID 3) in the same NAIP image. Source: MARS.
Table 1: MAP-3M provides 10x more images, and 100x more spatial coverages than literature datasets. GSD: meter/pixel. *; Cityscale contains 2K chips, we tile 224x224 for comparison.
Dataset
# Images
Image Size
Coverage Area
GSD
Building Cls
Road Cls
Cityscale He al. 2020a
49220
2
2470 km
1.0
X
SpaceNet3 SpacceNet 2018
2541
400x400
407 km3
1.0
X
AlCrowd (AlCrowd
2020
258044
300x300
2090 km
0.3
X
MAP-3M (Ours)
~3M
512x512
294069 kom?
Labels. MAP-3M collects vectorized annotations that cover two fundamental map classes: buildings and roads. Other classes such waterbodies, parking lot, etc. be sequentialized using the same representation methods (polygons/polylines) in Eq. 2 but are much less dominant, so we omit these map classes for now. Fig. shows the diversity and quality of the MAP-3M annotations.
In addition to the quality, MAP-3M has the largest quantity compared to literature map datasets, shown in Table [ The 3M positive samples 10x larger in number of images, and 100x

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larger in spatial area coverage than the previous largest dataset, which enables to conduct robust foundational model pre- trainingg and improve downstream generalization. Dataset has been released at /bttps://hugginn ace. ca/ datasets Labaa---aaaaaa MMPP-MMAPP- 3M
EXPERIMENTAL RESULTS
Experimental setup. For all model evaluations, we pretrained MARS on our MAP-3M dataset. For different downstream tasks, fine-tuned pretrained MARS these datasets and benchmarked its performance against specialized models. Three literature single-class benchmarks are used, together with our MAP-3M VAL
Cityscale (He et al. 2020a): An urban-scale benchmark for road graph extraction, structed from high-resolution aerial imagery.
SpaceNet (SpaceNel, 2018); A benchmark for road extraction from satellite imagery, with focus on generalization across geographies.
AICrowd (AICrowd, 2020): diverse dataset used in competitive challenges that covers building detection in complex urban scenes.
MAP3M-VAL: Our MAP-3M validation set containing two classes: building and roads. MАP3M-VAS VAL includes 5000 images separated from the train set.
5.1 FROM SINGLE TO MULTI-CLASS: TOWARDS UNIFIED MAP GENERATION
Figure 6 demonstrates the performance visualizations of MARS on handling multi-class geometry predictions. From simple single-pole peglllpo road, complex multi-polylines, and dense mixed buildings/roads, MARS learns to generate various map tiles in a unified end-to-end way.
To better understand the trade-off's and design choices in our modeling approach, we perform an ablation study comparing single-class and multi-class architectures in T As shown in Table the model scales well with increasing class diversity without obvious degradation in performance. As handling multiple map feature types within a unified framework is often more practical and efficient than deploying separate models for each class, such ontology scalability of MARS shows its great potential to serve as map foundational model for future development.
5.2 DOWNSTREAM FINETUNING COMPARISON
Roads. We employed TOPO, topological accuracy metric (He et al. 2020b), assess how closely the predicted road graphs align with the ground truth in terms of structural connectivity.
number moak stad-iinuge
zumber of buiddins/inage
E....
(a) Road Density Distribution
(b) Building Density Distribution
Annotation Visulization
Figure 5: MAP-3M features (a-b) wide map element density distribution per image, and (c) high- quality annotations with diverse vectorized map geometry coverage. Source: MARS.
Table 2: MARS architecture unifies single- multi-classes learning by simply adding class tokens.
Classes
MAP3M-VAL.
CITYSCALE
SPACENET
AICROWD
R
Fl
IOU
R
Fl
P
Fl
IOU
Road
89.8
85.6
87.7
85.1
78.2
81.5
79.3
83.3
81.2
Building
64.4
95.0
Both
90.1
77.1 1
83.1
61.0
84.3
81.5
82.9
79.7
84.6
82.1
97.3

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Figure 6: MARS visualization. MARS is capable of handling diverse scenes and geometries with- any post-processing, demonstrating the generalization and scalability of end-to-end learning framework. White dash-dotted line: Ground truth. Colored lines with asterisks: Predicted se- quences. Black circles with white numbers: Object IDs. Source: MARS.
Table 3: TOPO-based road performance comparison Cityscale and SpaceNet.
Model
CITYSCALE
SPACENET
R
F1
P
R
F1
Seg-UNet (Roneberrge et al.
2015)
75.34
65.99
70.36
68.96
66.32
67.61
Seg-DRM (Mättyus et al.
2017)
76.54
71.25
73.80
82.79
72.56
77.34
Seg-Improver Battr et al.
2019)
75.83
68.90
72.20
81.56
71.38
76.13
Seg-DLA Yu et al. 2018)
75.59
72.26
73.89
78.99
69.80
74.11
RoadTracer [Bastani al.
2018)
78.00
57.44
66.16
78.61
62.45
69.90
Sa12Graph (He et al.) (2020b
80.70
72.28
76.26
85.93
76.55
80.97
TD-Road (He et al. 2022)
81.94
71.63
76.43
84.81
77.80
81.15
RNGDet (Xu et al. 2022)
85.97
69.78
76.87
90.91
73.25
81.13
RNGDet++ [Xu et al. (2023)
85.65
72.58
78.44
91.34
75.24
82.51
RamRoad (Hetang et al. 2024
90.47
67.69
77.23
93.03
70.97
80.52
MARS
84.28
81.53
82.88
79.68
84.56
82.05
Table 4: Building performance comparison on Aicrowd V1.
Model
AICROWD-VI
AP
AP50
AP75
AR
AR50
AR75
bAP
IoU
C-IoU
PoLiS
N-ratio
PolyMapper
55.7
86.0
65.1
62.1
88.6
71.4
22.6
77.6
65.3
2.215
1.29
FFL*
67.0
92.1
75.6
73.2
93.5
81.1
34.4
84.3
73.8
1.945
1.13
Poly World
63.3
88.6
70.5
75.4
93.5
83.1
50.0
91.2
88.2
0.962
0.93
PolyBuilding
78.7
96.3
89.2
84.2
97.3
92.9
94.0
88.6
0.99
HiSup
79.4
92.7
85.3
81.5
93.1
86.7
66.5
94.3
89.6
0.726
Pix2Poly
79.6
91.6
85.2
87.7
95.03
89.85
0.479
1.111
GeoFormer
91.5
96.6
93.1
97.8
98.8
98.1
97.1
98.1
97.4
0.913
1.01
MARS
87.30
95.20
90.46
97.94
99.28
98.62
92.44
97.32
96.31
0.997
0.4542
As shown in Table 3 our model demonstrates superior performance compared to prior state-of- the-art approaches. On the Cityscale dataset, MARS achieves the highest Recall and Fl scores, outperforming RNGDet++ (Xu et al. 2023) by notable margin, from 78.44% % to 82.88 % in terms of F1. Similarly, on SpaceNet, MARS achieves leading recall 84.56%. and only -0.46 % in compared to the best priori method, RNGDet++ (Xu et al., 2023).
Of note, all prior methods Table 3 are road-specialized models rather than a general architec- ture that can model additional classes. Another trend observed is the contrast between architectural paradigms: MARS, being autoregressive, consistently achieves higher recall, while segmentation- based models like those in SamRoad (Hetang et al.) (024) tend to yield lower recall but compar- atively higher precision. Despite these differences, our model demonstrates superior balanced scores across datasets, outperforming previous state-of-the-ar approaches average.
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Buildings. To assess model performance the building class, we utilize standard metrics such as Average Precision (AP), Average Recall (AR), and Intersection over Union (loU), which are well- suited for evaluating polygonal predictions. We use AICrowd dataset V1 (AICrowd 2020) that has been widely adopted in prior research. Table summarizee our model's performance on AlCrowd- "imilarrrlly MARS achieves closely on-par model performance with the previous stateoof-the-are models despite being a completely generic map generation model without any sypar-parametens
Such performance highlights major promising advantages of MARS framework: (I) Simplicity: MARS handles all diverse geometry shapes without any specific post-processing or tuned hyperpa-
2024), including vertex confidence threshold tuning, non-maximung Saakimuu--uppressiooon IOU, etc. (2) Scalability: As MARS demonstrates
rameter like previous works (Hetang al.] 2024 Wang
can learn various fundamental geometry features: polylines, multi-polylines, and polygons, the model posses great potential to scale from current two-class to multi-class model, and finer-grained classification, such as highway vs pedestrian way. This indicates that MARS can serve as the foun- dational model for expanding range of future tasks related to map generation.
5.3 IMPORTANCE OF MAP-3M PRE-TRAINING
From experiments, find pretraining auto-regressive MARS will lead to significant faster vergence with a much higher accuracy e.g., from 70.45% % to 82.05% on SpaceNet (SpaceNet (018) shown in Table This challenge appears specifie training auto-regressive models: Unlike traditional rasterized segmentation approaches, MARS relies solely on next-token prediction, mak- ing them more sensitive data scarcity and harder to optimize from limited datasets. By contrast, with pre-training on MAP-3M, MARS quickly adapts and starts to pick-up target map features, highlighting the importance of large-scall pretraining for foundational model.
Table Effect of pretraining on downstream performance.
Model
SpaceNet
AICrowd
R
F1
I0U
V/O-Pretrain
77.62
64.48
70.45
95.09
W-Pretrain
79.68
84.56
82.05
95.24
5.4 CHAT WITH MARS
Model performance in real-world deployment can have out-of-domain generalization issues regard- less of architectural advances. Existing systems inevitably face such domain gaps that hinder the end-to-end mapping performance. Chat-with-MARS, as outlined in Section 3 enables users to guide and refine the model's predictions, providing a more responsive and adaptive solution. To assess its effectiveness, we present formal evaluations based on two following protocols:
Chat with point: In this setting, a single GT vertex corresponding to the starting point of missed road or building is introduced into new SOS inference pass as defined in Sec. 3.1 This mimics the "one-click" user behavior, where new minimal user input is used to guide the model in completing some missing map elements.
Chat with points: This configuration extends the protocol by providing two vertices of a missing object, forming a "two-click" setup that supplies a spatial direction. For both protocols, new predictions will be combined with old predictions for evaluation.
We quantitatively evaluate these two configurations for both road and building classes using the Cityscale (He et al., (020a), SpaceNet (SpaceNet, 2018), and SeCroodVV-ppattee (AICrowd) 2020) datasets (the subset of VAL images with missing building elements). As reported in Table 6 incor- porating user input consistently improves precision, recall, Fl score, and IoU across all datasets with diverse polygonal polyline structures, relative to the baseline model. These results demonstrate the efficacy of interactive support in enhancing model accuracy and robustness, also highlighting promising opportunities for further exploration of human-in-the-loop map generation.

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Published as a conference paper at ICLR 2026
Table 6: Performance of Chat with MARS across different datasets.
Model
CITYSCALE
SPACENET
AICROWD
P
R
F1
P
R
F1
IOU
MARS
84.28
81.53
82.88
79.68
84.56
82.31
97.32
Chat 1pt W MARS
84.95
82.66
83.79
81.40
84.65
82.95
97.35
Chat 2pts w MARS
85.15
83.14
84.13
81.74
85.12
83.17
97.40
5.5 LIMITATIONS
Computational efficiency. MARS's auto-regressive nature brings more computational overhead than the traditional iegmentation-based one shot methods, mainly because autoregressive decoding is inherently sequential. However, there have been many auto-regressive model acceleration tech- niques that greatly speedup the model efficiency such as KV-cache acceleration, parallel decoding, etc. We hope our first work establishes the architecture prototype for more future works in map generation foundational research improve both effectiveness and efficiency.
Challenging case visualizations. There are many challenging scenarios in our curated NAIP- 3M dataset. These challenge cases can pose great difficulties for both segmentation- based post- processing vectorization and auto-regressive vectorization methods, therefore we' d like to highlight: (i) Complex Road Intersections: In certain cases, our MARS model can produce correct intersec- tions; see Fig. 7 While in some cases, errors may occur when the model prioritizes the main roadway and overlooks thinner features. Representative failure cases are shown in Fig. 9 (a-c) in Appendix. A.3 (ii) Occluded Structures: heavy tree canopy shadows aries or road surfaces. Some failure cases are shown Fig. (d-f) in Appendix. A..] While these cases lead missed vertices, also include certain successful examples demonstrating that the model often learns to infer plausible shapes even under substantial occlusion. Examples of these cases are shown in Fig. in the same section.
obscure building bound-
6 CONCLUSION
In this paper, propose MARS: foundational auto-regressive map generation framework with three major contributions: (1) we proposed map-to-sequence conversion algorithm to address map generation from language-modeling perspective rather than visual perspective, a.k.a, treating maps as a foreign language; (2) we curated MAP-3M dataset that is 10x larger than the current biggest map dataset enable foundational model training: (3) finally, we present MARS that addresses map generation in an unified and end-to-end manner without any post- post-processigg The emerging "Chat with MARS" feature enables brand new human-ing -n-tn--i-te---oop vectorized map generation capability.
Disclaimer. This research is supported by the National Geospatial-Intelln Intelligence Agency (NGA) via Contract No. HM0476249 1002. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NGA, DoD, or the US government. Approved for public release, NGA-U-2025-02299
Acknowledgements. We would like to thank Raphael Tang, Xiaojie Guo, Mike Bianco, and Ja- cob Kovarskiy for their insightful feedback on the draft. We gratefully acknowledge support from Microsoft Spectre Al and Microsoft Planetary Computer.
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A APPENDIX
A.1 MAP-3M DATA CURATION
Data Collection Process. MAP-3M images are sourced from the National Agriculture Imagery Program (NAIP) (U.S. Department of Agriculture| 2025), known for its exceptional aerial imagery resolution at 0.6 meter per pixel (a small ratio of images be of 0.3 1.0 meter per pixel), facilitating detailed feature extraction and accurate object delineation. Leveraging population data from United States Cities Database (United States Cities Database 2025). we sample total of 5,000 cities from 50 states, proportionally distributing based on state-level populations. The geographic distribution is shown in Figure 5 (a). From each city, we collect the most recent image chip (usually of size 10k by 10k pixels, or 20k by 20k pixels) from time range of 2020 2024. We further tile each big image chip into 512x512 subtiles without overlapping.
Label Collection Process. Building annotations in MAP-3M are sourced from the Overture Map Foundation (Overture 2024), which currently has the most comprehensive building annota- tion across the globe by merging multiple authoritative and community-contribut datasets, in- cluding OpenStreetMap, Esri Community Maps, and machine-leaming-deriee building footprints from Google and Microsoft to fill in annotation gaps. Road annotations are sourced from Open- StreetMap (Map] 2017). encompassing all road subtypes such as highway, motorway, path, bridle- way, etc., thereby ensuring comprehensive coverage and diverse structural representation. Spatial and statistical distributions of MAP-3M are shown in Figure 5 covering a wide-range of diverse scenes.
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Table 7: Building performance Aicrowd V2
Model
AICROWD-V
AP
AP50 AP75 AR
AR50 AR75 bAp loU C-loU PoLiS N-ratio
MARS 30.91 57.06 30.07 70.03
86.31 73.85 89
74.42 66.49 4.135 1.012
p
Figure 7: MARS visualization. Robust handling of complex road intersections by MARS. White dash-dotted line: Ground truth. Colored lines with asterisks: Predicted sequences. Black circles with white numbers: Object IDs. Source: MARS.
Labeling Format. Annotations within MAP-3M adopt unified labeling format derived from the COCO standard (Lin et al.] 2014), facilitating streamlined integration into existing machine learning frameworks. Buildings are treated as different objects, and are provided with dynamic- length vertex sequences consistent with the COCO format. In contrast, roads undergo a conversion process wherein each image's road network is initially represented a flat graph, subsequently processed via our specialized stroke-based algorithms. The resultant separate road polylines are treated as different objects, with difference that their vertex sequences are open-ended. This unified representation ensures compatibility and simplifies multi-class annotation handling in downstream tasks. Dataset has been released.
A.2 AICROWDV2 VAL
To assess model performance the building class, we utilize standard metrics such Average Precision (AP), Average Recall (AR), and Intersection over Union (IoU), which are well-suited for evaluating polygonal predictions. Our evaluation spans two distinct test sets from the AICrowd dataset (AlCrowd] 2020): version (V1) and version 2 (V2). This dual evaluation approach is motivated by specitic characteristics of the dataset. V1 has been widely adopted in prior research, but recent findings in Pix2Poly (Adimoolam et al. 2025) suggests potential data leakage between its training and validation splits. V2 addresses this issue with revised structure, including new training and test splits. Notably, V1's validation set contains approximately 60,000 images, whereas V2's test set includes around 25,000 images. Due to lack of published benchmarks on V2, we believe results represent the first public evaluation this corrected version. Table summarizes our model's 's performance on AICrowd-Ve V1, while Table presents results on AICrowd-V2.
A.3 ADDITIONAL. RESULTS
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Published as a conference paper at ICLR 2026
Figure 8: MARS visualization. Robust handling of occluded structures by MARS. White dash- dotted line: Ground truth. Colored lines with asterisks: Predicted sequences. Black circles with white numbers: Object IDs. Source: MARS.
(3)
(b)
(c)
(d)
(A)
(
Figure 9: MARS visualization. Difficult examples like (a-c) complex intersections and (d-f) dense canopies occluding roads and buildings where MARS finds it difficult to preserve high accuracy. White dash-dotted line: Ground truth. Colored lines with asterisks: Predicted sequences. Black circles with white numbers: Object IDs. Source: MARS.
14


Reviews:
[{"review_id": "TxgGdKnSGV", "reviewer": "Reviewer_rikJ", "summary": "This paper proposes MARS, an end-to-end map auto-regressor for vectorized map generation. The key idea is to treat maps as a foreign language: all vector primitives (points, polylines/roads, polygons/buildings) are serialized via a map-to-sequence procedure, and then a vision encoder + autoregressive transformer decodes the whole map token by token. On top of that, the authors introduce “Chat with MARS”, an interactive, human-in-the-loop decoding mode (start-/mid-/end-of-sequence interventions) that can fix missing or drifting map objects with 1–2 clicks. To support this, the paper also curates MAP-3M, which is the largest aerial-image + multi-class (roads + buildings) dataset so far (∼3M tiles, wide US coverage, NAIP imagery + Overture/OSM labels). Experiments on Cityscale, SpaceNet, and AICrowd show that the unified, class-agnostic, autoregressive model is competitive with or close to SOTA methods that are narrowly specialized.", "questions": "* Will MAP-3M be truly public, with both imagery and vector labels, or only labels? Are there licensing constraints that will make the “3M” number lower in practice?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Framing vectorized map generation as AR sequence modeling is elegant: one decoder, one vocabulary, one loss, multiple geometry types. This is nicer than the usual “segmentation → heuristic post-processing” pipeline.\n* Even though the current demos look a bit toy-ish, the idea of AR decoding + teacher forcing ⇒ promptability is solid, and the paper shows three concrete intervention modes (SOS/MOS/EOS) with quantitative gains. This is, in my view, the most future-facing part.\n* A 3M-tile, dual-class, US-wide, reasonably high-res dataset that already comes vectorized would be very valuable to the community, especially for people doing OSM updating, change detection, and AD map pretraining. This alone can justify publication if it’s really as large, clean, and diverse as stated.\n* On Cityscale / SpaceNet they get very reasonable TOPO F1s, sometimes higher recall than road-specialized models, which is non-trivial for a generic AR model.", "weaknesses": "* Right now the two main claims — “we have a 3M, dual-class, high-quality dataset” and “we have a working general AR map model” — cannot be verified. In the last year I have seen many papers claim “large-scale dataset” and the final release was (i) much smaller, (ii) missing one of the modalities, or (iii) under a restrictive license. So the impact is contingent on actual release.\n* The whole “maps as a sequence” story stands or falls with the correctness of the stroke decomposition at intersections, roundabouts, and T-junctions. If this step introduces topology errors or weird ordering, the model will happily learn those artifacts. This should be stress-tested more.\n* The 1-click / 2-click “Chat with MARS” is impressive, but in the current form it is still a single-object recovery tool. For real OSM editing or AD map maintenance, users will want multi-round constraints (e.g., “keep these 3 roads, regenerate everything north of x”), not only point hints.\n* I suggest citing some downstream application papers like P-MapNet: Far-seeing map generator enhanced by both SDMap and HDMap priors.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ItZbhvbagc", "reviewer": "Reviewer_SQaC", "summary": "This paper proposes MARS, an auto-regressive foundational model for end-to-end vectorized map generation that unifies the prediction of roads (as multi-polylines) and buildings (as polygons) without relying on post-processing heuristics. The authors introduce a novel map-to-sequence representation, a large-scale multi-class dataset (MAP-3M), and a human-in-the-loop interaction paradigm called “Chat with MARS.” Sounds good.", "questions": "1. Can the dataset be generalized to global regions with different road/building styles?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This research topic is new to me.\n2. The writing is good.\n3. Interactive capability is intersting.", "weaknesses": "1. No ablation on the impact of the stroke-based decomposition vs. alternative graph traversal or serialization strategies\n2. Limited analysis of failure modes (e.g., complex intersections, occluded structures).\n3. The “Chat with MARS” evaluation is synthetic (uses GT points as prompts); real-user studies or robustness to noisy clicks would strengthen claims.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bYhc83dDXF", "reviewer": "Reviewer_nRw3", "summary": "This paper introduces a novel foundation model for map generation, termed MARS. The proposed approach employs a ViT backbone to extract visual features and an autoregressive transformer to generate map sequences, including points, polylines, and polygons. In addition, the authors present a multi-class map dataset to support the foundation model and benchmarking. The paper also introduces an interactive feature, Chat-with-MARS, which allows human-in-the-loop map generation and correction.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents MARS, a novel foundation model for vectorized map generation, which is both original and technically sound.\n2. The MARS framework demonstrates strong performance.\n3. The proposed Chat-with-MARS functionality is innovative and adds practical value by enabling interactive map generation.", "weaknesses": "1. While the authors provide detailed definitions of different map objects, autoregressive generation typically relies on a well-defined sequence order. However, the ordering of map objects in the proposed pipeline is not clearly explained.\n2. The use of a ViT-based visual backbone and an autoregressive transformer could make the model computationally expensive. The paper lacks discussion or analysis regarding computational efficiency or runtime performance.\n3. As a foundational model for map generation, the ablation studies are insufficient. The authors do not adequately examine the effects of architectural choices (e.g., different vision backbones or decoder designs) or training strategies.\n4. Although the Chat-with-MARS feature is compelling, the paper would benefit from additional demonstrations—such as a short demo video or a GUI prototype—to better illustrate its capabilities and user interaction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RiTG5rlNZa", "reviewer": "Reviewer_3621", "summary": "This paper introduces MARS, the first foundational model for vectorized map generation that unifies the creation of both road networks and building polygons within a single end-to-end framework. MARS treats vectorized map primitives as a language and employs a sequence-to-sequence auto-regressive Transformer to directly generate map elements without intermediate steps. The model is trained on a newly curated large-scale dataset, MAP-3M, which contains three million high-quality, multi-class map annotations—10× larger and 100× broader in coverage than existing benchmarks. Extensive experiments demonstrate that MARS outperforms prior rasterization-based and hybrid approaches while maintaining scalability and generalization. Additionally, the authors propose “Chat with MARS,” an interactive human-in-the-loop system that enables prompt-based map generation and correction.", "questions": "While MARS directly generates vectorized map elements through an end-to-end sequence model, it remains unclear how the proposed approach ensures geometric or topological boundary consistency between adjacent objects. Does the model incorporate any explicit mechanism or loss to preserve boundary alignment, or is this consistency purely learned implicitly from data?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. MARS  is the first foundational auto-regressive model for vectorized map generation, unifying both road networks and building polygons within a single end-to-end framework. The proposed map-to-sequence representation elegantly converts geometric primitives into a sequential language-like form, enabling map generation to benefit from advances in large-scale sequence modeling.\n2. The work not only presents a new modeling paradigm but also releases MAP-3M, the largest multi-class map dataset to date, supporting robust training and reproducibility. Extensive experiments demonstrate strong generalization and consistent performance gains over prior rasterization-based and hybrid approaches.\n3. The “Chat with MARS” module creatively leverages prompt-following capabilities of the auto-regressive model, introducing a novel human-in-the-loop mechanism for real-time map editing and correction. This interactivity significantly enhances the paper’s applicability to real-world geospatial workflows.", "weaknesses": "While the proposed map-to-sequence formulation is elegant, it inevitably flattens geometric structures into linear token sequences. The model relies mainly on data-driven regularities rather than structural constraints.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)